THE EFFECT OF CARDIONET HOME TELEMONITORING FOR CONGESTIVE

HEART FAILURE PATIENTS: AN OBSERVATIONAL RESEARCH STUDY

by

John R. Patrick

Copyright 2014

A Dissertation Presented in Partial Fulfillment

Of the Requirements for the Degree

Doctor of Health Administration

University of Phoenix

All rights reserved

INFORMATION TO ALL USERSThe quality of this reproduction is dependent upon the quality of the copy submitted.

In the unlikely event that the author did not send a complete manuscriptand there are missing pages, these will be noted. Also, if material had to be removed,

a note will indicate the deletion.

Microform Edition © ProQuest LLC.All rights reserved. This work is protected against

unauthorized copying under Title 17, United States Code

ProQuest LLC.789 East Eisenhower Parkway

P.O. Box 1346Ann Arbor, MI 48106 - 1346

UMI 3583294Published by ProQuest LLC (2014). Copyright in the Dissertation held by the Author.

UMI Number: 3583294

i

ABSTRACT

Congestive heart failure (CHF) afflicts millions of Americans, and accounts for the

largest share of rehospitalization of patients. Readmission rates for CHF patients have

been high for more than a decade, resulting in unfavorable outcomes for patients and

hospitals. One potential solution element is telemonitoring in the home. Allowing

cardiologists to monitor patients with chronic diseases remotely has been shown to

reduce hospital readmissions. This observational research (OR) study was based on

anonymous secondary data from a CardioNet telemonitoring study conducted by a

community teaching hospital in New England. The study was designed to answer the

research question of whether telemonitoring can predict an imminent heart failure

episode and, upon initiation of an intervention, reduce the number of hospital

readmissions. The OR study also reported the effect telemonitoring had on the number of

emergency department visits, medication changes, home healthcare visits, and visits to

cardiologists or primary care physicians. The study did not have a sufficient number of

participants to gain statistical power, but it highlighted the opportunity to learn more

about the population of CHF patients in the community. The study also identified an

opportunity for the use of mobile healthcare devices, big data, and analytics.

ii

DEDICATION

I dedicate this dissertation to my mother, Virginia Patrick. She was a loving wife

to my father for 60 years and a proud mother of her three sons. She passed away from

congestive heart failure in March 2009.

iii

ACKNOWLEDGEMENTS

I would like to thank my dissertation committee members: Dr. Ramin Ahmadi,

Dr. Damien Byas, and Dr. David Mohr for their support and tireless reading of

dissertation drafts. Their feedback was consistently valuable. A special thanks goes to

Dr. Mohr for his encouragement and his tireless assistance outside of the boundaries of

scheduled doctoral seminars. My gratitude extends to my wife, family, and friends for

their understanding during the past three-and-a-half years when various activities

received second priority after my studies. Although they have remained anonymous in

the dissertation, I would like to acknowledge and thank the medical and research staffs of

the community teaching hospital for their support and creation of the anonymized data

archive on which the study was based. I learned a lot from their many years of clinical

experience. I would also like to thank the development fund of the hospital and the

philanthropic community for the funding that makes it possible for the hospital to engage

in research studies. Finally, I would like to thank Anna McNamara from CardioNet for

her collaboration with the hospital that made the telemonitoring study possible.

iv

TABLE OF CONTENTS

ABSTRACT ......................................................................................................................... i

DEDICATION .................................................................................................................... ii

ACKNOWLEDGEMENTS ............................................................................................... iii

TABLE OF CONTENTS ................................................................................................... iv

LIST OF TABLES ............................................................................................................. ix

LIST OF FIGURES .............................................................................................................x

Chapter 1 ..............................................................................................................................1

Introduction ..........................................................................................................................1

Background of the Problem .................................................................................................1

The Human Heart and Congestive Heart Failure .....................................................3

CHF Admissions ......................................................................................................3

CHF Readmissions...................................................................................................4

Statement of the Problem .....................................................................................................4

Purpose of the Study ............................................................................................................5

Significance of the Problem .................................................................................................8

Overview of Research Methodology and Design ................................................................8

Observational Research Study .................................................................................9

Observational Research Questions and Hypotheses ................................................9

Method Appropriate to Purpose ...............................................................................9

Focus of the Design ...............................................................................................10

Theoretical Framework of the Observational Research Study ..............................13

Contribution to Knowledge....................................................................................13

v

Review of Relevant Scholarship ............................................................................14

Relevance of the Dissertation Research .................................................................14

Definition of Terms............................................................................................................15

Assumptions .......................................................................................................................17

Limitations .........................................................................................................................18

Delimitations ......................................................................................................................20

Summary ............................................................................................................................21

Chapter 2 ............................................................................................................................22

Literature Review...............................................................................................................22

Sources of Articles .............................................................................................................23

CHF ....................................................................................................................................24

CHF Readmissions.............................................................................................................25

Predicting Readmissions with Data .......................................................................25

Preventing readmissions ........................................................................................27

The Continuum of Care .....................................................................................................28

Care in the Hospital ...............................................................................................30

Care at a Residence ................................................................................................31

Role of Case Management .....................................................................................33

Emerging Role of PCMH.......................................................................................34

Health-Related Quality of Life ..............................................................................37

Patient Satisfaction.................................................................................................37

Telemonitoring ...................................................................................................................40

Telemonitoring Technology...................................................................................41

vi

CardioNet System Overview .................................................................................44

Benefits of Telemonitoring ....................................................................................49

Challenges to Widespread Adoption of Telemonitoring .......................................51

Relationship of Heart Activity and CHF Readmissions ....................................................61

Recruitment ........................................................................................................................63

Conclusion .........................................................................................................................65

Chapter 3 ............................................................................................................................67

Methodology ......................................................................................................................67

Research Method and Design Appropriateness .................................................................68

Research Questions and Hypotheses .....................................................................68

Method Appropriate to Purpose .............................................................................69

Focus of the Design ...............................................................................................71

Research Questions ................................................................................................71

Population and Sample ..........................................................................................71

Statistical Power.....................................................................................................73

Recruitment ............................................................................................................75

Informed Consent...................................................................................................75

Institutional Review Board ....................................................................................76

Confidentiality .......................................................................................................76

Instrumentation ......................................................................................................77

Data Collection ......................................................................................................78

Exits from the Study ..............................................................................................83

Validity and Reliability ..........................................................................................83

vii

Data Analysis .....................................................................................................................84

Conclusions ........................................................................................................................85

Chapter 4 ............................................................................................................................87

Results ................................................................................................................................87

Recruitment ........................................................................................................................88

Exclusions ..............................................................................................................88

Randomization .......................................................................................................90

Attrition ..................................................................................................................90

Sample Demographics and Characteristics ........................................................................92

Confounding Factors ..............................................................................................96

Variables ............................................................................................................................96

Data Analysis .....................................................................................................................97

Hospital Readmissions ...........................................................................................98

Medication Changes...............................................................................................98

Interventions by Nurses and PCPs .........................................................................99

Interventions by Cardiologists ...............................................................................99

Round-trip visits to the ED ..................................................................................101

Hypothesis Testing...............................................................................................102

Summary ..........................................................................................................................103

Chapter 5 ..........................................................................................................................104

Implications, Recommendations, and Conclusion ...........................................................104

Study Results ...................................................................................................................104

Discussion ........................................................................................................................107

viii

Relationship to Other Studies ..........................................................................................109

Strengths and Weaknesses ...............................................................................................110

Technology for Monitoring..............................................................................................112

Assumptions and Limitations ..........................................................................................114

Assumptions .........................................................................................................114

Limitations ...........................................................................................................114

Implications......................................................................................................................116

Proposed Future Research................................................................................................117

Recommendations for Healthcare Leadership .................................................................120

Conclusion .......................................................................................................................121

Author Biography ............................................................................................................147

ix

LIST OF TABLES

TABLE 1 ..............................................................................................................................24

SEARCH TERMS AND RESULTS ...............................................................................................24

TABLE 2 ..............................................................................................................................74

PATIENTS EXCLUDED ...........................................................................................................74

BASELINE CHARACTERISTICS OF STUDY PARTICIPANTS AS DESCRIBED IN TABLE 4. ............79

TABLE 4 ..............................................................................................................................80

BASELINE CHARACTERISTICS OF STUDY PARTICIPANTS ..........................................................80

TABLE 5 ..............................................................................................................................82

INTERVENTION MEASUREMENTS INCLUDED IN ARCHIVE OF SECONDARY DATA .......................82

TABLE 6 ..............................................................................................................................93

SUMMARY OF CATEGORICAL DATA FROM BASELINE CHARACTERISTICS ..................................93

TABLE 7 ..............................................................................................................................95

SUMMARY OF THE DISCRETE BASELINE CHARACTERISTICS ...................................................95

TABLE 8 ..............................................................................................................................96

SUMMARY OF THE DISCRETE BASELINE CHARACTERISTICS INCLUDING WITHDRAWALS ..........96

TABLE 9 ............................................................................................................................102

SUMMARY OF THE STATISTICAL TESTS FOR ALL DEPENDENT VARIABLES .............................102

x

LIST OF FIGURES

FIGURE 1. OBSERVATIONAL RESEARCH DESIGN ..............................................................12

FIGURE 2. CARDIONET MCOT™ PENDANT, THREE SENSORS, AND MONITOR .....................45

FIGURE 3. CARDIONET MCOT™ WIRELESS MONITOR ......................................................45

FIGURE 4. CARDIONET SYSTEM OVERVIEW........................................................................48

FIGURE 6. CHF PATIENT ADMISSIONS FROM SEPTEMBER 2009 THROUGH JUNE 2012 ........72

FIGURE 7. RECRUITMENT AND RANDOMIZATION ...............................................................89

1

Chapter 1

Introduction

Two people in America will experience a cardiac event every minute, and once

every minute, someone will die as a result (Roger & Turner, 2011). Congestive heart

failure (CHF) is a chronic disease that causes a disproportionately high cost of health care

for the elderly. CHF inflicts more than 5 million people in the United States (Scherr et

al., 2009), and because of an aging population, more than 400,000 new cases are

diagnosed each year (Dang, Dimmick, & Kelkar, 2009). CHF accounts for the largest

share of hospital discharges, and 10% to 50% of the discharged patients are readmitted

within six months of their initial hospitalization (Dang et al., 2009). Roger (2010) said

that CHF has become an emerging epidemic.

Background of the Problem

CHF is the leading cause of hospitalizations of the elderly (Jeon, Kraus, Jowsey,

& Glasgow, 2010). Many CHF patients receive good care while in the hospital (Joynt,

Orav, & Jha, 2011), but nearly one out of five Medicare patients discharged from

hospitals are readmitted within 30 days. CHF creates a poor quality of life for patients

and places an economic burden on the health care system (Ramani, Uber, & Mehra,

2010). The annual cost of readmissions to Medicare is $15 billion (Averill et al., 2009).

The high cost of readmissions has made payment reform a high-priority target for health

care policymakers (Vest, Gamm, Oxford, Gonzalez, & Slawson, 2010), and the center for

Medicare & Medicaid services (CMS) has begun implementation of penalties for

hospitals with excessive readmissions(Vest et al., 2010) (Aspenson & Hazary, 2012).

2

CMS has made it clear to hospitals that they will face increased financial responsibility

for CHF readmissions (Mulvany, 2009).

In the past, hospitals have been reimbursed for readmissions just like a normal

admission. However, the Patient Protection and Affordable Care Act (ACA) includes a

transition away from the traditional fee for service to a fee for value model. The shift

away from fee for service plus the penalties for an above average rate of readmissions

have caused (Hansen, Young, Hinami, Leung, & Williams, 2011) hospital administrators

to explore alternatives to manage the problem of high readmissions (Jweinat, 2010). Two

approaches that have shown positive results are increases in the amount of in-person

communications from caregivers and provision of multi-disciplinary coordinated teams

(Sochalski et al., 2009). Another approach that has shown promise is the use of statistical

models to predict preventable readmissions based on clinical logic (Goldfield, 2010).

Telemonitoring, the focus of this observational research (OR) study, is broadly

defined as the use of telecommunications to transfer information about the health status

of a patient from his or her place of residence to providers at a remote location (Maric,

Kaan, Ignaszewski, & Lear, 2009). Recent studies suggest that a telemonitoring-

facilitated collaboration between primary care physicians (PCPs) and a heart failure clinic

can reduce mortality and the number of days of hospitalization (Dendale et al., 2012).

Telemonitoring may have the potential to alert providers to intervene and prevent

readmissions.

Telemonitoring has shown promise as a tool to signal physiological changes in

CHF patients that could enable a caregiver to intervene and prevent a hospital admission

or readmission (Muller et al., 2010). Many reviews of telemonitoring focus on

3

telephone-based monitoring where a nurse may call the patient and gather data about the

patient’s condition or the patient may make a weekly call to an interactive voice response

(IVR) system (Maric et al., 2009). Advances in technology make it possible to cost-

effectively monitor electronic sensors and other devices to record physiological data

directly, including blood pressure, weight, or heart rhythm of a patient and transmit that

data using telecommunications technology to health care providers (2009).

The Human Heart and Congestive Heart Failure

Beginning before we are born and continuing until the last moment of our lives, a

pear-shaped muscular organ the size of our fist pumps blood to all parts of our body

(Heart, in anatomy, 2008). At some point in the aging process, the heart develops an

inability to pump sufficient amounts of blood to meet the demands of the body, resulting

in (CHF) (Congestive heart failure, 2008). Caregivers use a combination of behavioral,

pharmacological, device-based, and surgical treatments to reduce mortality and enhance

quality of life for CHF patients. Restriction on sodium intake and the administration of

diuretics to remove excess sodium and water from the body are commonly used to

prevent worsening of CHF symptoms.

CHF Admissions

Cardiovascular risk factors become increasingly prevalent as the population ages,

and as a result, health care professionals are encountering more patients at risk of heart

failure (Ramani et al., 2010). Hospital admissions continue to increase due to symptoms

that require emergency care (2010). The CHF admission rate among Medicare

beneficiaries 65 and over in 2008 was 3.4% (Health Indicators Warehouse, 2008).

4

A typical scenario is that an elderly person with CHF experiences dizziness or

shortness of breath while at his or her residence or at a nursing home or assisted living

home, and a caregiver calls the emergency medical services (EMS). An ambulance takes

the CHF patient to the emergency department (ED) of the hospital. The ED performs a

complete diagnostic evaluation and stabilizes the patient’s condition. The hospital’s

cardiac care unit typically admits the patient for further care.

CHF Readmissions

After several days of monitoring, adjusting medications, and stabilizing the

patient’s condition, the patient is typically discharged and returns to his or her place of

residence. Nearly 20% of patients in this scenario return to the hospital within 30 days

and 50% are readmitted with six months (Ramani et al., 2010). No single treatment is

known to be effective in preventing the readmissions (Hernandez et al., 2011), and

research into innovative treatment strategies is vital (Ramani et al., 2010).

Statement of the Problem

The general problem is that one out of five CHF patients discharged from the

hospital is readmitted within 30 days and 50% within 90 days (Averill et al., 2009). The

specific problem is that the frequent readmission of CHF patients to the hospital has a

negative impact on patients and hospitals. For the patients, readmissions result in

reduced quality of life for them and their families and have a negative impact on their

psychosocial and financial conditions (Ramani et al., 2010). Hospitals incur additional

cost for which they may not be reimbursed due to government changes in reimbursement

guidelines (2010). Readmissions increase demand for already crowded space in the ED

and thereby increase the need for capital for expansion.

5

Purpose of the Study

The primary purpose of this OR study was to compare the number of hospital

readmissions of CHF patients between those who received home telemonitoring versus

those who received usual care. Hospital administrators are looking for solutions to the

high cost of CHF readmissions, and telemonitoring is one alternative being considered

(Louis, Turner, Gretton, Baksh, & Cleland, 2003). If telemonitoring is effective, patients

and their families can benefit by having fewer return visits to the hospital. The OR study

informs administrators and clinicians about the potential impact on these goals.

Researchers have performed a large number of telemonitoring studies with CHF

patients. The results have been mixed. Clarke, Shah, and Sharma (2011) performed a

systematic review of 125 articles about randomized controlled experiments that were

designed to determine if telemonitoring was effective for patients inflicted with CHF.

Their review concluded that telemonitoring, in conjunction with home health care and

specialist support, can be effective in the clinical management of patients with CHF, and

have a positive impact on quality of life. A large study funded by the National Heart,

Lung, and Blood Institute (People Science Health, 2012) and supported by Yale

University included 1,600 patients (Chaudhry et al., 2010). The study concluded that

telemonitoring had no significant effect on the readmission rates of the patients.

Most of the studies reviewed included monitoring of physiological measurements

of a patient such as weight, blood pressure, and oxygenation. Other studies have used

telemonitoring to collect data by gathering input from the patient such as how he or she

was feeling, whether he or she took their medications, or if his or her diet changed. A

study including 43 patients used a handheld device called the Health Buddy® to

6

interactively ask questions of the patients Karg (2012). Although some studies suggested

telemonitoring has potential, such as a telemonitoring study with 160 patients by Dendale

et al. (2012) which showed reduced hospital readmissions and improved mortality, none

were conclusive about the value. This OR study examined the value of monitoring the

patient’s heart activity as a possible predictor of an impending hospital readmission.

I examined, retrospectively, data obtained from a cardiac telemonitoring research

(CT) study conducted by a community teaching hospital in New England (CTH). The

CT study commenced in March 2013 and data collection was completed in November

2013. The CTH then made anonymized secondary archival data from its study available

for the OR study. The archival data did not contain any personally identifiable health

care information. I did not perform any data collection or research procedures for the

telemonitoring research study, and the hospital approved the access to the anonymized

archival data used in the OR study.

The CT study included two randomly selected groups of participants: a usual care

group (UCG) that received treatment for symptoms that caused their hospital admission,

and a telemonitoring care group (TCG) that received telemonitoring in addition to the

usual care. In addition to examining the effects of telemonitoring, hospital researchers

will study numerous clinical and biological factors on an on-going basis to look for

relationships among pre-existing patient conditions, determine the effects of various

medications, and look for meaningful patterns of heart activity. The OR study included

an examination of a subset of the data that was collected as described in chapter 4.

The OR study is independent from the hospital research and I was not included on

the hospital’s research protocol. The OR study focus was primarily on the question of

7

whether the use of telemonitoring can result in reduced hospital readmissions and to what

degree telemonitoring influenced the number and type of patient interventions. The OR

study was aimed at identifying whether telemonitoring results in changes to the

management of care, which is of great interest to hospital leaders and administrators.

Although telemonitoring is a medical practice tool that enables a health care provider to

remotely monitor the condition of patients, the focus of the OR study was whether

telemonitoring may also be used to assist hospital administrators in achieving reduced

readmissions.

Some studies have shown that a risk assessment while the patient is in the hospital

can provide reliable data to the patient’s primary care physician (PCP) after discharge

(Bird, Noronha, & Sinnott, 2010). The hospital recommends a timely appointment with

the PCP within a week of discharge to take advantage of the risk assessment on behalf of

the patient. The home health care services (HHCS) team has a positive impact on quality

of life and can assist in the interim care of a CHF patient. Telemonitoring can potentially

provide patient information to a provider that can enable interventions at the residence of

a patient instead of a readmission to a hospital. In summary, the purpose of the OR study

was to examine secondary archival data from the CTH study and determine if

telemonitoring would be a meaningful supplement to the care of CHF patients and result

in a lower rate of readmissions.

8

Significance of the Problem

The increased focus from research studies and from CMS has caused all hospitals

to strive to reduce readmissions. The U.S. government, the largest payer in health care,

has identified readmissions as a major factor in driving health care costs upward. If the

research hypothesis in the OR study--decreased readmissions demonstrated through

telemonitoring--is true, hospitals and payers will have another tool to reduce the growing

costs.

Hospital administrators are facing decreasing reimbursements, increasing costs,

and increasing demand. The only way a hospital can survive in the changed health care

environment is to cut costs. CHF readmissions are a significant source of controllable

cost for hospitals. The federal cost is tens of billions of dollars per year. Providers and

payers care about any solutions that can help reduce cost.

If the OR study hypothesis is true, hospital administrators will find it

advantageous to implement telemonitoring for the majority of CHF patients. The result

for patients could be an improvement in their quality of life, and for hospitals that

implement telemonitoring in a cost-effective way, they should experience a reduction in

their financial risk and gain the capacity to invest additional funds in their mission to

enhance the health of the communities they serve. If the hypothesis is not rejected,

hospitals can avoid the capital expense and staff resources to implement a telemonitoring

program.

Overview of Research Methodology and Design

The OR study used quantitative analyses to investigate the relationship between

telemonitoring of a patient’s heart and hospital readmissions. The study included a

9

retrospective examination and analysis of archival data from the CT study. The

following paragraphs provide an overview of the research design of the OR study.

Observational Research Study

The OR study examined the effectiveness of home-based telemonitoring of the

TCG in providing actionable data to care providers that could result in reduced hospital

readmissions for patients with CHF compared with the UCG. Related questions of

interest included the number and type of interventions that occurred. The primary

question was whether the telemonitoring data could help predict an impending problem

that a cardiologist could address, in lieu of calling EMS followed by hospital

readmission. The primary measure of the effectiveness of these actions was the 30-day

all-cause readmission rate of patients.

Observational Research Questions and Hypotheses

Ho1: The null hypothesis is that there is no significant difference in CHF patient

readmissions to the hospital between the TCG and the UCG.

Ha1: The alternative hypothesis is that there is a significant difference in the

number of readmissions in the TCG.

H02: The null hypothesis is that there is no significant difference in the number

or type of interventions between the TCG and the UCG.

Ha2: The alternative hypothesis is that there is a significant difference in the

number or type of interventions between the TCG and the UCG.

Method Appropriate to Purpose

Mann (2003) said that observational research is a useful research method to

retrospectively compare two groups with the objective of identifying predictors of an

10

outcome. Observational research is a type of correlational non-experimental research in

which a researcher observes behavior or variables over time. There are various types of

types of observational research including naturalistic observation where a researcher

observers ongoing behavior, participant observation where the researcher inserts himself

or herself as a member of a group in order to observe behavior, and archival research that

involves retrospective analysis of existing data. With the need to use anonymous

secondary data and inability to manipulate the independent variable, the OR study using

archival data proved to be an effective method.

OR designs are quantitative but are not experimental. Unlike in experimental

designs, the observational researcher does not manipulate the independent variable and

observe the effect on dependent variables (Fitzpatrick & Wallace, 2006). Since this OR

study did not include manipulation of any variables nor have access to any primary data,

the observational design was well suited. The observational design method is to

retrospectively examine anonymized archival data and investigate whether there are

statistically significant relationships among the variables. See Figure 1 for a diagram of

the process that was used to analyze the archival data from the CT study. Although cause

and effect cannot be determined with an observational design, the design can identify

relationships among variables and can be useful in suggesting additional hypotheses

(Mann, 2003).

Focus of the Design

The focus of the OR study design was the effectiveness of telemonitoring as an

aid leading to interventions that reduce hospital readmissions. The primary endpoint of

the study was the 30-day all-cause hospital readmissions. Secondary variables that were

11

measured included the number of interventions by type (medication changes, visits by a

nurse, visits to a PCP, visits to a specialist, round-trip visits to the ED, calls to an EMS,

or no action taken). The primary independent variable was the use of telemonitoring.

Anonymized archival data were analyzed to look for relationships between the variables.

12

Figure 1. Observational Research Design

13

Theoretical Framework of the Observational Research Study

A recent large study of the impact of telemonitoring on CHF readmissions

concluded that telemonitoring had no impact on patient outcomes (Chaudhry et al.,

2010). The study was based on a randomized controlled experiment where patients were

divided into two groups: usual care and care with telemonitoring. The telemonitoring

was performed using an IVR system that patients called to report their condition. The

study concluded that there was no significant relationship between telemonitoring and

readmissions. What differentiates the OR study is that it evaluated the effectiveness of

cardiac telemonitoring technology not previously used with CHF patients as a tool to

reduce readmissions. Most studies to date have used either telephonic data gathering or

traditional once-daily monitoring of weight, blood pressure, pulse and oxygenation

(Polisena et al., 2010). Some studies were supplemented with interactive questions to the

patient. These methods require significant patient involvement that can have a negative

impact on participation and the accuracy of the data.

Contribution to Knowledge

The OR study examined the effectiveness of cardiac telemonitoring as a tool to

offer providers an opportunity to follow interventions that may reduce hospital

readmissions for CHF patients. CardioNet, the company that makes the telemonitoring

technology, said that their products and services had not previously been used with CHF

patients for reducing readmissions. The insight gained about the variables in the OR

study may be valuable to hospital administrators to help them evaluate the use of

telemonitoring for care management that may help them to reduce costs.

14

The OR study provides knowledge about the potential advantages of more

recently developed technology that can continuously monitor cardiac activity. The OR

study provided additional knowledge about the relationships between the variables and

results. The relationship between the primary dependent variable and the independent

variable of telemonitoring and the relationship with the secondary dependent variables

are discussed in chapter 4.

Review of Relevant Scholarship

The literature review is contained in chapter 2. Relevant databases were searched

to identify literature that is appropriate for the planning of the dissertation research.

EBSCOhost, ProQuest, and PubMed are the primary database sources used, but health

care-specific databases such as Medline and the National Center for Health Statistics

were also used. Key search areas included the human heart, heart failure, congestive

heart failure, CHF care, home health care, and telemonitoring. Specific search terms

included congestive heart failure, CHF, managing CHF, heart failure, hospital

readmissions, telemonitoring, home health monitoring, and home health care for CHF.

Relevance of the Dissertation Research

Berkman and Abrams (1986) reported that the hospital readmission rate for

cardiac patients was 25%. Two and a half decades later, nearly 20% of CHF patients are

readmitted to the hospital within 30 days and 50% are readmitted within six months

(Ramani et al., 2010). Readmissions have become a major focus area as studies continue

to validate that there are a large number of high cost and potentially avoidable events.

Vest et al. (2010) said that hospital readmissions are a leading topic of discussion among

health care policy makers. Annual cost of billions of dollars and readmissions almost as

15

high as 25 years ago suggest that the problem is well identified, but the solution is not.

The dissertation research adds new insight that can provide hospital administrators with

an additional tool to aid in solving the problem.

Definition of Terms

Congestive heart failure (CHF): A healthy individual’s heart can tolerate

significant demands over a considerable length of time. For those with CHF, their heart

is unable to expel and circulate sufficient blood to keep pace with the demands of the

body (Congestive heart failure, 2008).

Home Health Care Services (HHCS): HHCS are services delivered in a patient's

home or place of residence. The caregivers typically include registered nurses,

nutritionists, social workers, home health aides, and therapy staff (physical, occupational,

speech) (Austin & Wetle, 2012).

Integrated care: Integrated care means that a caregiver coordinates patient care

across the continuum of care. The patient-centered medical home (PCMH) can serve this

purpose.

Patient Centered Medical Home (PCMH): The PCMH is a primary care physician

(PCP)-centered model of care in which the PCP acts as the integrator to assess the needs

of patients and refer and coordinate their care with the appropriate care providers (Shi &

Singh, 2011).

Primary Care Physician (PCP): A PCP is the first health care provider a

consumer normally sees for assistance with an illness, injury, or for preventive services

(Greenwald, 2010).

16

Randomized Controlled Experiment (RCE): An RCE is an experiment in which

chance is introduced when assigning subjects to either a treatment group or a

control group (Christensen, Johnson, & Turner, 2011).

Readmission: When a patient is discharged from the hospital and subsequently is

readmitted, the episode is called a readmission (Jencks, Williams, & Coleman, 2009).

Rehospitalization: A rehospitalization is the result of a hospital readmission

(Jencks et al., 2009).

Telehealth: Telehealth is the use of telecommunications technologies and the

Internet to support a broad range of services for consumers and professionals including

long-distance clinical health care, health-related education, and public health information.

Technologies include videoconferencing, the World Wide Web, streaming media, and

land-based and wireless communications (HRSA, 2012).

Telemedicine: Telemedicine is a subset of telehealth that uses telecommunications

and the Internet for diagnosis, consultation, information exchange, supervision, and

assessment of health. Telemedicine can be as simple as two doctors having a conference

call via telephone to discuss the diagnosis of a patient or as sophisticated as a surgical

procedure performed where the surgeon is in different location than the patient (Centers

for Medicare & Medicaid Services, 2012c).

Telemonitoring: Telemonitoring is the use of electronic sensors and other devices

to record physiological data such as the blood pressure, weight, or heart rhythm of a

patient and transmit that data using telecommunications technology to health care

providers (Maric et al., 2009).

17

Telemonitoring care group (TCG): The TCG includes patients who receive a risk

assessment before discharge from the hospital, a personalized care plan, and

telemonitoring.

Usual care group (UCG): The UCG includes patients who receive a risk

assessment before discharge from the hospital and a personalized care plan.

Assumptions

Assumptions are so basic that if a researcher did not establish any, the research

questions could not be answered with reliable meaning (Leedy & Ormrod, 2005). The

assumptions here are intended to prevent any misunderstandings about the results of the

OR study. The first assumption was that telemonitoring is a method of clinical health

care that is acceptable to the various Federal and State regulatory bodies and the relevant

medical boards and credentialing authorities. This assumption was proven valid because

the CTH has a contract with CardioNet--unrelated to the study--for the use of

telemonitoring on an inpatient or in-home basis. The local visiting nurses association

(VNA) also provides in-home telemonitoring.

A major assumption was that the telemonitoring would perform as described in

the CT research design. It was assumed that CardioNet’s Mobile Cardiac Outpatient

Telemetry™ (MCOT)TM technology accurately collects data about the patient’s heart

activity, the monitor algorithm accurately identifies a significant change in heart activity,

and that the monitor transmits the data to the CardioNet datacenter without losing any

fidelity. It was further assumed that the CardioNet monitoring technicians would

properly analyze the data and respond to events, and that accurate data would be reported

18

to health care professionals as called for in the research design. The CardioNet

equipment and services performed as expected, consistent with the assumptions.

Another major assumption was that CTH would properly train the HHCS staff

and unaffiliated providers to ensure they would be competent to execute the care

protocols outlined in the research design. There were no reported issues or shortcomings

related to this assumption. A final assumption was that the CHF patients selected for the

study would be representative of the larger population of CHF patients in the United

States. The validity of this assumption was limited by the small size of the sample

resulting from significant exclusions. The impact of not being able to confirm this

assumption limited the generalizability of the study, but the conclusions and

recommendations from the study remain relevant to administrators and researchers.

Limitations

The primary limitation of the OR study was the quantity and quality of data made

available from the CT study. CTH has sole authority over the recruitment of participants

and the duration of their study. Without sufficient archival data, it is not possible to make

statistically significant conclusions about the relationship among the research variables.

The CT research design called for the intervention by a cardiologist when

indicated by data from telemonitoring. The reliability of the study results is dependent on

consistent implementation of the intervention protocol for all events. It was possible that

providers would need to interrupt or disregard the planned protocol in the interest of

patient safety. This limitation was manifested in the high number of patients that were

deemed by the PI to be too sick to participate in the study. This resulted in a high number

of exclusions as discussed in chapter 4.

19

It was possible that a patient in the study could be readmitted at a hospital other

than CTH. CTH has a significant market share in the area it serves and there are no other

hospitals in the immediate area, but a patient could be out of town on a visit and need

EMS that did not go to CTH. Although patients are urged to keep their MCOT™

monitor close by, there was no requirement that they stay close to home.

The research design of the CTH study assumed that patients in the TCG and the

UCG would receive the same care. It was possible that interventions would occur that

were beyond the control of the study. For example, a patient could have a son who is a

cardiologist who visited him or her every day. Another patient could have lived at the

home of a daughter who is a retired coronary care unit nurse. A patient could have lived

in an undesirable environment that offered poor care, or a noisy and frenetic atmosphere

that would not be conducive to recovery from the most recent episode. A multitude of

other scenarios could have existed that would have detracted from or amplified good

care. The CT study design attempted to compensate for these variations through

randomization. Any non-standard care should have been equally likely to occur in the

TCG as in the UCG.

An additional limitation was the support of CTH. CTH clinical and

administrative support was essential to the execution of the CTH study and the

production of the research data. The support included personnel from the departments of

emergency medicine, cardiology, nursing, hospitalist care, home health care, financial

services, graduate medical education and research, and network operations. It was

possible that financial, organizational, or clinical priorities could have changed and CTH

may have had to withdraw its support of their study.

20

The strategy to mitigate the limitation of the number of patients that would be

recruited for the study was to lengthen the study duration. The length of the study was

increased substantially toward this end, but the elongation of the study reached the limits

of the hospital budgetary and personnel support. Chapter 5 includes recommendations

for subsequent similar studies.

Delimitations

The CT study focused on patients with CHF as a primary diagnosis. Patients that

have a primary diagnosis with other common chronic diseases such as chronic obstructive

pulmonary disease (COPD) or diabetes may also have CHF, but they were not included

in the study. However, no patient was excluded if they had comorbid conditions as long

as the primary diagnosis was CHF. A second delimitation was that the primary

dependent variable in the OR study was a readmission to the hospital within 30 days for

any reason. For example, it is possible that a CHF patient may get dizzy, fall at their

residence, be taken to the ED, and be admitted to the hospital. For purposes of the OR

study, such a case was considered a readmission. This is consistent with CMS plans to

adjust payments to hospitals for excessive readmissions, regardless of the cause (Health

Reform GPS, 2011).

Mortality is cited as an important dependent variable in many studies. Measuring

mortality requires a longer study and was not a focus of this study. If a patient should die

during the course of the study, he or she would not have been included in the study

results.

The OR study has a single primary dependent variable, which is the 30-day all-

cause readmission rate. Secondary dependent variables of interest include interventions

21

by type (medication changes, visit by nurse, visit to PCP, visit to specialist, round-trip

visit to ED, call to EMS, or no action).

Summary

Leedy and Ormrod (2005) said that the world is full of problems that beg for

solutions, and consequently the world is full of research activity. This is certainly true in

the area of health care. Research studies designed and performed well have the

possibility to produce solutions that hospital administrators can implement to improve

quality, patient safety, and financial risk. CHF readmission is a high-priority problem for

all hospitals and the OR study has the potential to contribute important knowledge that

can enable hospital administrators to develop solutions.

22

Chapter 2

Literature Review

More than 1 out of 3 Americans have some form of cardiovascular disease (Roger

& Turner, 2011), and CHF is the leading cause of hospitalizations of the elderly (Jeon et

al., 2010). More than one million hospitalizations for CHF per year cost nearly $29

billion (Roger & Turner, 2011). Hospitals discharge approximately one out of five

patients covered by Medicare and then readmit them within 30 days. The annual cost to

Medicare of these admissions is estimated to be $15 billion (Averill et al., 2009). The

impact of rehospitalization has caused hospital administrators and researchers to look for

solutions.

The purpose of the literature review is to investigate the research performed by

others and discover if they were able to find a relationship between the use of home-

based telemonitoring and reduced hospital readmissions for patients with CHF. An

analysis of the research done by others can help establish a theoretical basis about a

potential relationship between telemonitoring and readmissions. Learning from the work

of others can help in evaluating a problem and discovering what research techniques

worked and where gaps in knowledge may exist (Leedy & Ormrod, 2005). The literature

review is organized into sections including sources of articles and a set of topics. The

topics include CHF, CHF readmissions, CHF continuum of care, telemonitoring,

telemonitoring with integrated care, the relationship between heart activity and

readmissions, and recruitment.

23

Sources of Articles

An extensive search in EBSCO, PubMed, Medline, and ProQuest databases

revealed many relevant research articles. Searches were made using keywords that relate

to the research question. The five major variables of interest include CHF, CHF

Continuum of Care, readmissions, telemonitoring, and home health care. Because of the

breadth and depth of health care, many terms have similar or overlapping terms with the

same meaning, and this was considered in developing the search terms used to identify

relevant journal articles. The initial search for CHF was performed using a search term

equal to congestive heart failure OR chronic heart failure OR chf. Results from this

search yielded 41,260 journal articles published within the past five years. The search

results were then filtered for those articles that included the search term equal to

readmission OR rehospitalization with a result of 1,354 articles. A third search included

filtering using the search terms of telehealth OR telemedicine OR telemonitoring OR

telecardiology. The search produced a match with 368 articles. Final filtering used a

search term equal to home and produced a match with 169 articles that are the primary

focus of the literature review. See Table 1 for a list of search terms and results.

24

Table 1

Search Terms and Results

Search Term Results

((congestive+heart+failure) OR (chronic+heart+failure) OR chf) 41,260

Filtered: readmission OR rehospitalization 1,354

Filtered: telemedicine OR telehealth OR telemonitoring OR

telecardiology

368

Filtered with home 169

CHF

Hospitalization for CHF increased 300-400% between 1971 and 1999 (Zhang &

Watanabe-Galloway, 2008), and data from the National Hospital Discharge Survey show

that the hospitalization rate continued to rise significantly between 1995 and 2004 (Zhang

& Watanabe-Galloway, 2008). CHF is a chronic affliction that affects millions of

Americans imposes a significant burden on the health care system and causes patients to

have a reduced quality of life (Cherofsky, Onua, Sawo, Slavin, & Levin, 2011). The

Agency for Healthcare Research and Quality (AHRQ), estimated that the cost for

hospitalization of elderly patients with CHF between 1997 and 2004 increased 48%, from

slightly more than $6,000 per stay to slightly more than $9,000. In addition to the high

financial costs of CHF, the disease takes a large toll on the quality of life (QOL) of

patients (Farcaş & Năstasă, 2011). QOL refers to a combination of psychological and

physiological factors that influence a patient’s perception of his or her life. CHF patients

experience frequent visits to the hospital, shortness of breath, dizziness, depression, and

concern for their other comorbidities (Jenkins & Kirk, 2010). Cycling and recycling in

and out of hospitals can be emotionally upsetting, especially for elderly patients, and can

25

increase the chance of experiencing medical errors because of the complexity of care

coordination (Mor, Intrator, Feng, & Grabowski, 2010).

CHF Readmissions

Readmissions to the hospital have negative impacts on hospitals and patients, and

researchers are searching for cause and effect relationships to mitigate the problem. After

reviewing 43 articles and 12 specific activities, Hansen et al. (2011) found that no single

intervention reduced 30-day rehospitalization with any regularity. Hospital administrators

are also focused on readmissions as part of their efforts to reduce costs (Epstein, 2009).

Healthcare leaders must anticipate that health care reform will include better alignment

between incentives, reimbursements, and readmissions. Jweinat (2010) recommended a

strategy that includes identifying those patients who are at risk for readmission, based on

the severity of their illness, availability of follow-on care, and various socio-demographic

factors.

Predicting Readmissions with Data

Goldfield used mathematical models to predict impending readmissions

(Goldfield, 2010), and concluded that improvements in hospital quality of care are not

sufficient to reduce readmissions. Goldfield (2010) suggested that financial incentives

may be required and CTH that incentives could be funded by imposing a penalty on

providers that have a higher than average readmission rate. Incentives could be provided

to those hospitals that have a lower readmission rate than their peers or to PCPs who

demonstrate effectiveness in coordinating care for CHF patients.

On the surface, readmissions look like bad behavior on the part of hospitals, and

policymakers are urging changes (Berenson, Paulus, & Kalman, 2012). It is true that,

26

unless a hospital is at full capacity, the current system provides no motivation to avoid

readmissions because hospitals are paid for the services they provide. However,

beginning in 2013, for hospitals that exceed the average readmission rates for certain

chronic illnesses, the ACA provides for a penalty of 1% growing to 3% in 2015. If a

hospital’s readmission rate for certain diseases, including CHF, exceeds the national

average, the penalty was implemented by CMS reducing Medicare reimbursements to the

hospital in the following year. Proponents argue this will change hospital behavior.

Others believe that there may be unintended consequences such as hospitals working on

improving the numbers without actually improving care and patient safety (Joynt & Jha,

2012). The worst scenario is that hospitals that serve populations with poor and mentally

ill patients stop providing an important service by not readmitting these patients.

Hasan et al. (2010) studied the possibility of general predictors for hospital

readmissions. The study included 10,946 patients discharged from six academic medical

centers. The patients had been admitted for general medicine purposes. Four categories

of patients were identified: health care received, health condition, social support, and

socio-demographics. The readmission rate for the study participants was 17.5% and the

researchers were able to develop a correlation between a score within the patient category

and the likelihood of a readmission. The researchers developed a logistic regression

model to predict readmissions. The model was based on a scoring system to differentiate

patients. For example, a patient with Medicare got 5 points and a patient with self-

insurance got 2 points. A married patient got 2 points and a non-married patient got no

points. Similar relationships were developed for patients with or without a regular

physician, varying comorbidities, number of admissions within the past year, and the

27

length of the current hospital stay. Patients with a score above 25 had double the

readmission rate compared to patients who had a score less than 25. Prediction models

such as this could be useful to hospital administrators, but it does not inform specific

CHF factors that might be predictive.

Allaudeen et al. (2011) evaluated whether nurses, case managers, and physicians

could predict whether patients would be readmitted based on their clinical knowledge and

familiarity with the patients. The researchers concluded that none of the healthcare

providers could predict which patients would experience a high risk of readmission. The

researchers said that hospitals have no accurate predictive tools at their disposal.

A study in Spain including 394 heart failure patients examined whether health-

related quality of life (HRQL) could be a predictor of hospital readmissions (Rodriguez-

Artalejo et al., 2005). A heart failure-specific instrument, the Minnesota Living With

Heart Failure (MLWHF) questionnaire was used to calculate hazard ratios, which might

predict impending hospitalization based on HRQL scores. The study results showed a

lower HRQL score was associated with hospital readmission and death in patients with

heart failure.

Preventing readmissions

CHF is a condition for which hospitalization is often preventable with appropriate

care (Zhang & Watanabe-Galloway, 2008), but readmission rates have been high for

many years without improvement (Epstein, 2009). Therefore, prediction of readmissions

must be supplemented with prevention of readmissions. Hines, Yu, and Randall (2010)

suggested that tracking of quality of care, patient satisfaction, and quality of life could

help justify the investment in improved heart failure management programs, and serve as

28

an ingredient in the prevention of readmissions. As cited by Goldfield (2010), good care

while in the hospital is not sufficient to prevent readmissions.

Active participation of the patient and his or her family, deployment of home

health nurses, and multidisciplinary care coordination are essential (Hines et al., 2010).

Continuous contact with the patient by the family and across the continuum of care keeps

the patient informed and motivated. Bowles, Holland, & Horowitz (2009) found that

more frequent in-person visits correlated with reduced readmissions.

Mor et al. (2010) said that five conditions account for most readmissions: urinary

tract infection, sepsis, electrolyte imbalance, CHF, and respiratory infection. The

researchers retrieved CMS data for 2000 to 2006 for all skilled nursing facility episodes

that occurred during the years that were within thirty days of discharge from the hospital.

The researchers asserted that the primary cause of the readmissions is that Medicare

payment incentives have not encouraged providers to coordinate the care of beneficiaries.

The researchers estimated that 78% of 30-day discharges to nursing homes could be

avoided by revising the incentives.

The Continuum of Care

The traditional mode of clinical care is for each provider to treat a patient

independently (Nutting et al., 2010). A patient goes to a PCP, is treated, and returns to

home as though a transaction has been completed. Likewise, a patient may go to the ED

of a hospital and be admitted. The hospital performs various tests and treatments and

then discharges the patient--transaction completed. Greenwald (2010) described how

such lack of coordination and patient data result in a poor continuity of care. A care

philosophy that consider all aspects of the health of a patient from physical, emotional,

29

and financial points of view is referred to as the continuum of care (Austin & Wetle,

2012). A key ingredient to facilitating the continuum of care is the handover or handoff.

Hesselink et al. (2012) said that one of the most important elements to reducing hospital

readmissions is to have an effective handoff between the hospital and the PCP and home

health services.

Many intervention strategies to reduce readmission rates have focused on patient

education and a structured plan of care to ease the transition to home following discharge

from the hospital (Slyer, Concert, Eusebio, Rogers, & Singleton, 2011). Healthcare

providers believe that coordination of care plans by nurses can improve medication

reconciliation, patient education, communications, and follow-up. However, there have

not been systematic studies to support the conclusion that nurse coordinated care for CHF

patients can reduce readmission rates (Slyer et al., 2011).

A group of community based and specialty health care providers near Melbourne,

Australia formed a consortium to reduce the demand by CHF patients on the ED and

inpatient hospital services (Bird et al., 2010). A team of medical consultants and multi-

disciplinary specialists designed a comprehensive model of care for patients who were

recruited to the project. Care Facilitators identified unique health care needs beyond the

normal care and offered information, education, and advice to the patients. Statistical

analyses were performed using patient records of patients in the project and those who

had declined the recruitment. The results showed that patients in the project had

substantially less presentations to the ED, fewer inpatient admissions, and a smaller

number of inpatient days. The results suggested that patient-focused integrated care with

education and self-management should be part of any CHF care program.

30

MaineHealth established a Planned Care Model that replaced the previous silos of

care with an integrated, coordinated, standardized, and reliable system of care (Cawley &

Grantham, 2011). Administrators developed relationships between healthcare providers

and champions from diverse care settings. Designated champions helped MaineHealth

create comprehensive strategies that linked CHF services across the entire health system.

Although the metrics have not yet validated improved outcomes, MaineHealth has been

successful in overcoming structural and cultural barriers to increase collaboration and

communication across the CHF continuum of care, which they believe will lead to lower

readmissions.

The literature provides numerous references to the role of the PCMH and the

accountable care organization (ACO) as models for delivering coordination and

integration across the continuum. Shugarman and Whitenhill (2011) described how the

ACA reinforces these concepts and how CMS is providing grants and incentives to

establish trials. The PCMH is a logical coordination point for the treatment of CHF

patients (Shugarman & Whitenhill, 2011).

Care in the Hospital

Joynt et al. (2011) performed a retrospective cohort study with 4,095 U.S.

hospitals to investigate whether large hospitals provided better care than small hospitals.

The patient outcomes studied were for Medicare enrollees with a primary diagnosis at

discharge of CHF. The results showed a direct relationship between process measures

and the volume of patients. High-volume hospitals scored better than low volume

hospitals. For patients in the hospitals that had a lower volume, they experienced higher

costs, a higher mortality, and higher readmissions than those patients who were admitted

31

to high-volume hospitals. Mortality and cost of care was more favorable in the medium

and high-volume hospital groups. The conclusion was that hospitals with more volume,

and hence more experience, produced higher quality care and better outcomes, but at a

higher cost.

Shafazand et al. (2012) examined the question of whether all CHF patients who

present to the ED need to be admitted. In a study of 2,648 patients, the researchers

determined that only 2% could be discharged directly from the ED after treatment and

without admission to the hospital. The primary reason for not being able to avoid

admission was the comorbidities of the patients.

The most important factor in hospital care for CHF may not be the care received

in the hospital but the planning and coordination of the care that follows discharge from

the hospital. An important element of discharge planning is the establishment of a

follow-up appointment with a PCP, but equally important is the recommendations made

to the patient about how they should care for themselves. Andrietta, Lopes-Moreira, &

Bottura Leite de Barros (2011) examined 14 papers on the subject of discharge planning

and found that when nurses include educational information in the discharge plans,

patients are more effective with their self-care.

Care at a Residence

Short-term medical care is generally provided at a hospital. However, there are

risks of infection and medical error in a hospital scene that could make care at a residence

more desirable (Tibaldi et al., 2009). Researchers designed a prospective, single-blind,

randomized controlled experiment including 101 CHF patients more than 75 with a six-

month follow-up. Patients were randomized into two care groups: one at a general

32

medical floor at the hospital and the other with care at home assisted by the Geriatric

Home Hospitalization Service (GHHS). The GHHS provided therapeutic and diagnostic

support in the patient’s home. After six months, the groups showed no significant

differences between readmissions or mortality. There were encouraging signs with

regard to the home health care group. Although the readmission rate was no different,

the time to the first readmission was 84 days for those at home versus 69.8 days for those

discharged from the hospital (Tibaldi et al., 2009). There were other encouraging signs

about the home health care group. The home care group had improved nutritional status,

quality of life, and improvements in depression. Tibaldi et al. (2009) concluded that

home care is a viable substitute for traditional hospital care. The study did not examine

the cost differences between the home care and hospital care. A hospital may have

economy of scale advantages by taking care of multiple patients in one setting versus

home health care clinicians who would have to travel from house to house.

Telemonitoring may be a productivity enhancement for the home health care model.

Bowles (2009) examined home CHF care across three modalities: only nurse

visits, nurse visits plus intervention by telephone, and nurse visits with telemonitoring.

Approximately 300 patients were randomized to equal groups. Initial results showed that

the nurse-only visits group had significantly lower hospital readmissions. However, after

adjusting for the number of visits and patient diagnoses, the differences were non-

significant.

Affordability and accessibility to care for CHF are issues in some urban areas of

America (Bakhshi et al., 2011). Home care supplemented with telemonitoring may be a

good option in such situations. An initial pilot study of 44 CHF patients in urban Denver

33

showed favorable results with fewer hospital days historically and compared to a control

group. Unfortunately, the study was small and had no statistical significance.

Another factor in the care of CHF at home is the support of family members.

Kang, Li, and Nolan (2011) said that family support can provide significant improvement

in CHF disease management. However, in many cases, the families are not sufficiently

informed in how to provide the care, which can be complex if comorbidities exist. Kang,

Li, and Nolan (2011) concluded that more studies are needed to understand the

requirements for family member training.

Role of Case Management

Nurses with broad experience in managing patient cases can develop creative

strategies that result in positive patient outcomes and cost-effectiveness. Historically, the

hospital would treat a CHF patient and discharge him or her to their place of residence.

With the focus on high readmissions, policymakers and administrators have focused on

more effective discharge planning. The goal is to provide more than a discharge by

providing a transition to continued care at home. The most significant elements of

discharge planning provided by nurse coordinators include educational content, a follow-

up appointment with a PCP, and an outline of home care recommended to be followed by

the patient with assistance from family members (Slyer et al., 2011). One of the most

challenging elements of discharge planning is medication reconciliation (Hansen et al.,

2011). CHF patients often have more than a dozen medications at the time of admission.

Hospitals change medications to conform to their formularies. At the time of discharge,

34

the patient often needs assistance to reconcile what medications he or she should take

upon returning home.

Case managers must be well informed about the frequent changes in payer

incentives and reimbursement policies. A narrow focus on clinical care is not adequate --

case managers must take responsibility for initiatives across the continuum of care that

affect fiscal results. Approaches to reducing readmissions that have shown positive

results include increasing the amount of in-person communications from caregivers and

provision of multi-disciplinary coordinated teams (Wade et al., 2011). Telemonitoring

can help initiate the in-person interventions by transferring information about the health

status of a patient from his or her place of residence to providers at a remote location.

Support staff can review the data and upon seeing a pattern that is unusual, can alert

providers to intervene and prevent readmissions. Sochalksi et al. (2009) performed a

randomized controlled experiment with the goal of assessing changes in clinical

outcomes and quality of life because of telemonitoring. The results showed no

discernible difference in mortality or morbidity of the patients. However, the

telemonitoring resulted in more engagement of the home health care nurses, which in turn

resulted in less time in the hospital.

Emerging Role of PCMH

The negative impact of rehospitalization on patients and hospitals has caused

hospital administrators and researchers to look for solutions. A common theme among

proposed solutions is better cooperation between health care organizations across the

continuum of care. Hines, Yu, and Randall (2010) said that key elements of the

coordination should be wellness management, multidisciplinary care, comprehensive case

35

management, and active participation of nurses and family members. No provider can

offer all the services that CHF patients need. The patient-centered medical home

(PCMH) concept is part of current health care reform and provides the potential to be the

coordination point to bring all the necessary provider resources to bear on the needs of

the patient (Roberto et al., 2010). By making the transition from an entitlement-oriented

fee-for-service-based model to an accountability-oriented fee-for-value-based model,

patients should receive higher quality care at affordable prices, as envisioned by the

ACA.

Piterman et al. (2005) outlined recommendations for providing optimized care for

the increased prevalence of CHF. They highlighted that pharmacological treatments are

important but suggested that the coordination role of the PCP is most critical. The article

recommends that the care model should be evidence-based and provide an integrated care

model including the patient, family and other disciplines working as a team to maximize

treatment alternatives. The team should include the PCP, cardiologist, home health care

clinicians and nurses, pharmacist, community caregivers, and the patient. The article is

slightly dated but also suggested that a telephone support system should be part of the

integrated care. Placing the PCP in the role of integrated care coordinator is a logical

suggestion, except for the fact that a major shortage of PCPs will be caused by the ACA

(Jacobson & Jazowski, 2011). Jacobson and Jazowski (2011) said a new strategy is

needed to meet the emerging PCP shortage. The PCMH has the potential to fill the gap.

The National Demonstration Project (NDP) was initiated in 2006 to serve as a

national evaluation of the PCMH model. A diverse mix of 36 family practices were part

of a qualitative study in which evaluation teams read interviews, observations, emails,

36

and other sources to summarize the patterns and activities of the practices. Six themes

emerged from the study and they suggested that transformation from current practice

models to the PCMH will require much more than structural and organizational changes

(Nutting et al., 2010). The PCMH requires an attitudinal shift where clinicians work as a

team seeking to create value for patients as opposed to the historical model of creating

volumes of treatments, procedures, tests, and return visits. The entitlement-oriented fee-

for-service-based model must shift to an accountability-oriented fee-for-value-based

model.

In addition to the organizational and attitudinal changes that the PCMH model

will require, there are considerable IT implications (Bates & Bitton, 2010). To ensure the

efficiency, safety, and quality that PCMHs will require, EMR systems will need to be

enhanced with new features. The EMR for patients whose health is being coordinated by

a PCMH will need to provide for telemonitoring data, measurement of quality and

efficiency, identification of care transitions, personal health records created by patients,

registries of diseases, collaborative care data, and clinical decision support systems for

the management of chronic diseases (Bates & Bitton, 2010).

The American Academy of Family Physicians (AAFP) adopted a policy that

every American should have a “personal medical home” (Stream, 2012). The AAFP has

evangelized the PCMH as a means to achieve lower health care cost and improved

outcomes for patients. Stream (2012) said that PCMHs are starting to pay dividends, and

described several examples of health plans that are paying bonuses to providers who

embrace and successfully implement the PCMH concept. For example, six health

plans paid $1.5 million to 236 PCPs from 11 primary care practices in the Hudson

37

Valley in New York after the National Committee for Quality Assurance (NCQA)

recognized those practices for exemplary operations.

Health-Related Quality of Life

Clinicians have many tools to measure and report the condition of a patient. For

example, imaging studies or laboratory results indicate the health of a patient.

Researchers have shown that another important dimension of measuring the health of a

patient is to get input directly from the patient about how he or she feels (Dunderdalea,

Thompson, Milesc, Beerd, & Furzec, 2005). The patient’s perspective is now viewed as

being as valid and important as the prognostication of the health care provider. The

American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR,

2012) recommended that providers measure health related quality of life (HRQL) for all

cardiovascular or pulmonary patients (Lefebvre et al., 2010). CHF can have a severe

effect on a person’s quality of life by reducing their independence and capability to enjoy

the basic activities he or she has been accustomed to (Yun-Hee, Kraus, Jowsey, &

Glasgow, 2010). Measurement of HRQL is most meaningful in studies that are of

significant duration. Thus, HRQL measurements were not used in this study, which is

focused on 30-day outcomes for patients.

Patient Satisfaction

One of the many new policies contained in the ACA is the intent to introduce

incentives for improved health care (Doherty, 2010). Beginning next year, CMS is

withholding 1% of hospital reimbursements and redistributing the withheld funds based

on a hospital score. The score is 70% based on quality measurements and 30% on patient

satisfaction (Centers for Medicare & Medicaid Services, 2012b). The concept behind

38

HCAHPS is to provide an incentive to shift from a volume based reimbursement to a

value based reimbursement, with patient satisfaction comprising an important element of

the incentive. CMS describes the new methodology as value based purchasing (VBP) of

health care (VanLare & Conway, 2012).

Patient satisfaction as a key metric in health care is relatively new in the U.S., but

has now become quite important. Otani, Waterman, and Dunagan (2012) examined

patient satisfaction data from more than 32,000 hospital discharge cases in Texas with a

goal of determining if the severity of a patient’s illness affected satisfaction with the care

he or she received. The study results showed wide variance in satisfaction and indicated

that severity alone did not affect satisfaction. The largest factor affecting satisfaction was

the quality of nursing care. Taylor (2012) wrote that for every patient that complained

about the service they received, 26 remained silent. Twenty-five of the 26 who remained

silent each told 15 people about the poor service they received. Only six satisfied

patients told someone else about their positive experience. The 26 dissatisfied patients

never used the provider again. Like in other industries, hospital executives are realizing

that patient satisfaction is directly related to revenue.

At CTH, the quality committee of the board of directors reviews key quality

metrics quarterly, including the new HCAHPS consumer survey. The questions asked in

the consumer survey are very specific about the hospital and staff. Questions focus on

whether the room was clean and quiet and whether physicians and nurses communicated

effectively. The patient satisfaction survey represents a dramatic change in direction for

health care. In effect, customers will directly influence the financial results of the

hospital based on their survey input. The voice of the customer is being heard.

39

For executives at CTH, success is measured by the board through a quarterly

review of a dashboard they call “Vital Signs”. Seven key signs are used to evaluate

results and to provide a component of the variable portion of the senior management

compensation. One of the seven areas is quality and includes the HCAHPS patient

satisfaction score.

The Centers for Medicare & Medicaid Services (CMS) first released the

HCAHPS survey instrument in November 2005. It was developed through a public and

collaborative process that included scientific research, field testing with consumers, and

multiple opportunities for public comment (Centers for Medicare & Medicaid Services,

2012a). The HCAHPS survey contains 18 patient perspectives on care across eight

topical areas. The survey instrument captures the essence of the hospital portion of the

patient experience. There are specific survey vendors that are approved for use by

hospitals. In the case of CTH, it has used Press Ganey for quite a few years and it is one

of the approved vendors. Hospitals must develop relationship-marketing programs to

communicate the importance of physician and nurse relationships with patients. They

will need to enhance communications programs with patients to ensure that patients

understand what treatments they are receiving and why. Social media will play an

increasing role (Friedman, Gyr, & Gyr, 2010).

One tool that hospitals are turning to for increasing focus on the patient is

Planetree--a model of care that puts the focus on the patient as never before. Shi and

Singh (2011) described the model as patient centered. Stichler (2011) added that

Planetree is family-centered. Focusing on the patient as the center of health care services

is directly associated with positive patient satisfaction (Cliff, 2012). In a comparison of

40

HCAHPS scores between a group of Planetree hospitals and the CMS national average,

the Planetree hospitals had substantially higher scores in all ten domains measured (Cliff,

2012).

Physicians, nurses, and management at CTH have been trained on the Planetree

model and learned the importance of treating patients with respect and ensuring that their

communications with patients are clear and unambiguous. Planetree studies show that by

asking the patient if he or she understand what has been explained to them, there is a net

savings of time by avoiding follow-on questions later (Stichler, 2011). CTH expects that

its move to becoming a Planetree hospital will play an important role in helping it to gain

good survey scores.

Telemonitoring

Telehealth is the use of telecommunications technologies and the Internet to

support a broad range of services for consumers and professionals including the

distribution of public health information, availability of health-related education, and

long-distance clinical health care diagnostics and monitoring. Technologies include land-

based and wireless communications, the World Wide Web, streaming media and

videoconferencing (HRSA, 2012). Telemedicine is a more specific subset of telehealth

that uses telecommunications and the Internet to provide access to online consultation,

diagnosis, health assessment, intervention, coordinated care plans, and the ability to

exchange information with providers. Telemedicine can be as simple as two doctors

having a conference call via telephone to discuss the diagnosis of a patient or as

sophisticated as a surgical procedure performed where the surgeon is in a different

location than the patient (Centers for Medicare & Medicaid Services, 2012c).

41

Telemonitoring is a subset of telemedicine that uses electronic sensors and other digital

devices to record physiological data, such as the weight, heart rhythm, and blood

pressure, and transmit that data to health care providers using telecommunications

technology, (Maric et al., 2009).

Telemonitoring Technology

The days following discharge from the hospital require close monitoring to

prevent mortality and rehospitalization (Stoyanov & Paul, 2012). Monitoring of the

physiological status is important for predicting hospital admissions because of heart

failure. There is a range of technology approaches to telemonitoring. The IVR approach

is among the simplest. The patient dials a number and answers pre-programmed

questions about their condition. A more personal approach is to use a structured phone

interview conducted by a care provider.

Cardiac devices implanted in the patient are gaining use by some practitioners

(Stoyanov & Paul, 2012). A German study of 29 patients used data from implantable

devices combined with weight and blood pressure readings to look for relationships that

might be predictive of rehospitalization (Lieback, Proff, Wessel, Fleck, & Gotze, 2012).

Although the results were inconclusive, the researchers noted the value of the data if

compliance was high and the data were gathered and monitored continuously.

Implantable devices are not yet used for routine telemonitoring.

Advances in electronic technology have made it possible for telemonitoring to use

devices with embedded programmed intelligence – often called smart devices. Patients at

home are monitored by the devices, which provide data to providers via the Internet. If

abnormalities are detected, a provider can call or visit to provide an intervention for the

42

patient. The following are three telemonitoring offerings approved by the Federal Drug

Administration (FDA).

Corventis has developed the AVIVO® Mobile Patient Management (MPM)

System (Corventis, 2012). The AVIVO MPM System provides continuous remote

telemonitoring of the key vital signs of a patient and helps physicians track the patient’s

health status and detect a deterioration of the patient’s condition or potential health risks.

The monitoring of a patient is done with a Band-Aid®-like sensor called the PiiX®.

Because the unobtrusive PiiX® has no electrical leads and is water water-resistant, it has

the potential to achieve high patient compliance. The PiiX® sensor picks up

physiological signals including heart rate, heart rate variability, fluid status, respiratory

rate, activity level, and posture. Corventis has developed proprietary algorithms

embedded in the PiiX that can detect arrhythmias. The patient data are transmitted to

Corventis where physicians can view physiological trends and ECGs via a secure Web

portal.

MedApps has developed product and service lines called the Remote Health

Monitoring system (MedApps, 2012). Similar to CardioNet and Corventis, the MedApps

system is based on the automatic transmission of physiological readings from sensors to a

secure server. MedApps inserts the data received at the server into the electronic health

record of the patient being monitored. Unlike CardioNet and Corventis, which have

proprietary sensors, the MedApps remote health monitoring system works with a broad

range of MedApps and non-MedApps sensors for monitoring of glucose, blood pressure,

weight, and blood oxygen saturation. MedApps has developed their CloudCare™

Platform to accommodate a wide range of health monitors and sensors.

43

The CardioNet MCOT™ technology is an ambulatory cardiac monitoring service

with real-time analysis of heart beats, automatic detection of arrhythmia, and wireless

transmission of electrocardiography (ECG) data (CardioNet, 2012). The patient wears a

pendant around his or her neck that has three electrodes each with a lead attached to the

pendant. The electrodes send ECG information about every heartbeat to a small portable

monitor. If the monitor detects an abnormality, it automatically sends the ECG

information to the CardioNet monitoring center. Certified monitoring technicians

analyze the data, respond to events, and report data to health care professionals. A home

health care nurse, the patient-centered medical home (PCMH), PCP, or cardiac specialist

can have 24x7 access to a web portal that contains patient data, similar to what would be

available if the patient was in a coronary care unit at a hospital. Early warning is

important for CHF patients because it is the gradual buildup of congestion that can cause

decompensated heart failure (Polisena et al., 2010).

The primary function of the CardioNet MCOT™ is to detect the presence of

arrhythmia, an irregularity in the rhythm of a person’s heartbeat (American Heart

Association, 2012). Among other purposes, ECG monitoring is used as a diagnostic tool

for examining patients that have a blackout suspected to be the result of something wrong

with the heart. However, in certain cases, the traditional ECG with multiple wires

connected from a patient to a computer on a cart or the wall of a hospital room, is not

able to facilitate the correct diagnosis. In cases such as these, an alternative tool for

diagnosis is the loop recorder. The loop recorder is accurate, but it is expensive and

requires an implantation in the patient (Petkar, Cooper, & Fitzpatrick, 2006). CardioNet

44

developed the MCOT™ service as a less expensive and less invasive alternative to the

loop recorder.

CardioNet System Overview

Patients using the CardioNet telemonitoring service wear a pendant around the

neck that has three leads each attached to a small electrode that is affixed to the patient’s

chest. See Figure 2. The three electrodes transmit two channels of ECG data. The

reason there are two channels is to provide two views of the heart activity. This can

enable a physician to differentiate activity between upper and lower chambers of the

heart.

Basic heart beat data and a heart rate trend chart showing high, low and average

heart rates are collected in the CardioNet monitor and transmitted to the CardioNet

monitoring center during the night. Additional data are sent to CardioNet whenever the

patient has a symptom and presses a button on the monitor. Cardiologists can establish

triggers for various heart activities as part of a telemonitoring plan, and the monitor will

transmit these data whenever a trigger is activated.

45

Figure 2. CardioNet MCOT™ pendant, three sensors, and monitor

The three electrodes detect each heartbeat and the pendant produces two channels

of ECG information. The ECG data are anonymously sent to the monitor using a 900

MHz wireless signal similar to how a cordless telephone communicates with a base

station. The monitor, see Figure 3, contains a microprocessor that runs a proprietary

algorithm that is able to detect and analyze the heart rhythm. If the monitor detects an

abnormality, it automatically sends the ECG information to the CardioNet monitoring

center, otherwise the data are stored in the monitor until the next regularly scheduled

transmission. The monitor can store 31 days of data.

Figure 3. CardioNet MCOT™ Wireless Monitor

The MCOT™ Monitor transmits the data it has collected using a cellular wireless

connection over the Internet to Sprint, CardioNet’s wireless business partner. The

Internet connection uses a virtual private network (VPN), often called a tunnel, encrypted

46

and dedicated to communications only between two specific devices: the MCOT™

monitor and a server at Sprint. In effect, the VPN provides a private tunnel across the

Internet (VPNC, 2012).

Although Sprint has significant coverage throughout the United States (Sprint,

2012), some patients may be at a location that does not have access to a Sprint cellular

signal. As a backup to a cellular connection, the CardioNet MCOT™ monitor can

transmit the ECG data to a base station using Bluetooth (UCLA, 2012). The base station

serves as a charger for the monitor, but also can be connected to a landline wall jack. The

base station contains a modem that sends the data to Sprint using an automatically dialed

connection. All CardioNet data transfer and storage is HIPAA compliant. At no time

during any type of data transmission is any Protected Health Information (PHI) sent or

received by the MCOT™ device.

The Sprint datacenter receives the data from all MCOT™ devices worn by

patients and transfers the data to CardioNet’s monitoring center over a secure private

network. Sprint has no visibility to patient information, nor can Sprint correlate any

MCOT™ device to a specific patient. The transmission of the data to CardioNet’s

monitoring center is also transmitted to a redundant backup data center to ensure

continuity in the event of a disaster that impacts the monitoring center.

CardioNet’s monitoring center in Conshohocken, Pennsylvania processes the

ECG data and creates patient-specific reports that are sent to physicians via secure FAX

transmission or are securely downloaded by the physician using CardioNet Access, a

HIPAA- compliant encrypted Web portal. Certified monitoring technicians observe the

data, make comparisons to prior data, respond directly to patients if indicated, and

47

provide health care providers with reports and analysis as appropriate. See Figure 4 for a

pictorial diagram of the CardioNet MCOT™ system.

48

Figure 4. CardioNet System Overview

49

A study in Philadelphia including 266 patients concluded that the MCOT™

technology provided a more accurate detection of clinically significant arrhythmias and a

shorter time to diagnosis than the loop recorder (Rothman et al., 2007). The success of

the Rothman and colleagues (2007) study helped establish CardioNet as a public

company focused on helping physicians diagnose and treat patients with arrhythmias.

Only two other research studies involving CardioNet MCOT™ were found in the

literature. Saarel (2008) found that CardioNet MCOT™ was safe and effective when

used with children and adolescents with suspected arrhythmia. A European study used

MCOT™ with 19 patients who were diagnosed with a heart condition called atrial

fibrillation (AF). The patients in the study were subjected to a technique called ablation,

where biological tissue is removed as part of a treatment. The study confirmed that the

wireless telemetry from MCOT™ proved useful for follow-up monitoring of patients

who receive this particular type of treatment (Vasamreddy et al., 2006).

No studies could be found in the literature involving MCOT™ and CHF, nor

CardioNet and CHF. Anna McNamara, RN, the senior vice president for clinical

operations at CardioNet, Inc. confirmed that the company was not aware of any studies

involving CHF (A. McNamara, personal communication, July 9, 2012).

Benefits of Telemonitoring

Clarke, Shah, and Sharma (2011) performed a systematic review of 125 articles

that described randomized controlled experiments (RCEs) designed to evaluate whether

telemonitoring was effective for patients with CHF. The RCE studies included at least 50

CHF patients. The researchers excluded studies that did not explain the nature of the care

provided to patients in the control group and studies that used telephone support but no

50

telemonitoring equipment. After applying these extraction rules, the researchers analyzed

the outcomes of the remaining 13 studies. The selected studies included more than 3,000

patients, and the follow-up period of the studies was 3-15 months. The researchers found

a reduction in mortality in the studies they examined, but hospital admissions were no

lower than in patients without telemonitoring. The review concluded that telemonitoring,

in conjunction with the support of technical specialists and home health care providers,

can have a positive impact on quality of life and serve as an effective tool for clinical care

and management of patients with CHF.

Dendale et al. (2012) investigated the use of telemonitoring as a tool for intensive

follow-up with CHF patients. The researchers examined if telemonitoring facilitated

collaboration between a heart failure clinic and primary care physicians. The goal was to

determine if the collaboration would result in reduced hospital readmissions and

improved mortality. A sample of 160 patients was randomized into usual care and

telemonitoring-supported care groups. The results showed that mortality and days lost to

hospitalization were significantly lower for the telemonitoring-supported group.

In addition to studying the effectiveness of telemonitoring, a systematic review of

telemonitoring studies by Louis et al. (2003) examined related questions of interest

including whether patients receiving telemonitoring have an enhanced quality of life, and

whether the cost of care was less than with normal care. Some studies in the systematic

review included ancillary information about how the home health care providers used

telemonitoring data, and described if the telemonitoring data facilitated interventions.

Antonicelli et al. (2008) examined the effects of home telemonitoring on a

randomized sample of 57 elderly CHF patients with congestive heart failure (CHF). The

51

patients in the telemonitoring and usual care groups were followed for 12 months and

then compared on the basis of compliance with treatment, hospital readmissions,

mortality, cost of care management, and quality of life. In the telemonitoring group,

weekly reports were generated showing the patient clinical status, and care managers then

modified the patient’s care plan based on the reports. Patients in the telemonitoring group

had lower readmissions and lower mortality. Their better compliance with treatment

plans was indicated by more frequent use of prescribed medications and lower

cholesterol. The telemonitoring group also had a more positive perception of their health.

The researchers concluded that the better results in the telemonitoring group were due to

the improved treatment compliance and the more frequent availability of patient clinical

data to care management because of telemonitoring.

Dar et al. (2009) recruited 182 CHF patients discharged from three acute care

hospitals in the United Kingdom for an RCE with usual and telemonitoring groups. The

study results showed no difference in mortality, but the telemonitoring group had

significantly fewer visits to clinics and EDs and lower hospital readmissions. Although

there was no significant difference in the measureable cost of care, the researchers

concluded that telemonitoring could offer the benefit of enabling physicians to increase

the number of CHF patients they could manage under their care.

Challenges to Widespread Adoption of Telemonitoring

Picture archiving and communication systems (PACS) have resulted in the

widespread adoption of teleradiology (Reiner, 2008), but other forms of telemedicine

have stalled (Zanaboni & Wootton, 2012). Farzanfar, Finkelstein, and Friedman (2004)

observed that adoption of telemonitoring by providers is dependent on adoption by

52

patients. If patients find the telemonitoring technology too complicated or cumbersome,

they will reject it, making telemonitoring ineffective for the provider. The researchers

conducted in-depth interviews of patients who were using two different telemonitoring

approaches for the control of asthma. In both systems, the patients found the interactions

complex and not designed with the patient’s needs in mind. Nangalia, Prytherch and

Smith (2010) conducted a health technology assessment review of home telemonitoring

and found additional challenges that impede adoption including the non-availability of a

comprehensive range of needed sensors, size and bulkiness of sensors and monitors,

various networking inadequacies, and costs. The following sections address these and

other inhibitors to widespread adoption of telemonitoring.

Financial and regulatory frameworks. A supportive planning and

reimbursement framework is necessary to ease the way for providers to widely adopt new

technologies such as telemonitoring (Straub, Haas, & Mex, 2006). Policymakers and

providers must remove the barriers to telehealth adoption so that it can be made available

to all. Zanaboni and Wootton (2012) said that adequate professional and financial

incentives should be considered as a way to provide motivation for widespread adoption.

One significant barrier to adoption has been that a hospital or critical access hospital

(CAH) had to follow a bureaucratic privileging and credentialing process for practitioner

or physician that would provide telemedicine services to patients. CMS has developed a

new rule that is expected to remove the financial and productivity hardship (Telemedicine

Credentialing and Privileging, 2011).

Telemonitoring presents an unclear picture of legal responsibilities. For example,

a telemonitoring program may involve a patient in one state, a hospital in another state,

53

and a vendor in a third state collecting the telemonitoring data. Each state has laws and

regulations that may conflict with those in the other states. Zigmond (2012) said that in

spite of the legal uncertainties in advanced telemedicine, CMS has launched a one billion

dollar grant program in 2012 to spur telemedicine innovation in the care of children.

Training, Trust, and Confidence. Home health care and community nursing

resources manage telemonitoring deployment. Successful adoption requires that these

resources be trained so that they can facilitate the implementation and maintenance of

telemonitoring programs (Vincent, Reinharz, Deaudelin, Garceau, & Talbot, 2007). The

providers must believe that telemonitoring is for the benefit of the patient and not a

method to measure the effectiveness of the provider. They must also be confident that

the data gathered from telemonitoring is secure and will not be misused. Sharma,

Barnett, and Clarke (2010) reported that a perception of trust and security must be present

with the providers for telemonitoring to be embraced and adopted.

Gagnon, et al. (2012) developed a technology acceptance model (TAM) and used

a panel of experts to evaluate 234 questionnaires retrieved from doctors and nurses across

multiple departments of a tertiary hospital. The conclusion of the study was that the only

variable that was predictive of telemonitoring adoption was the perception among

clinicians. If those in the provider organization facilitating the telemonitoring

implementation were perceived to have adequate skills and resources to support the

clinicians, the clinicians were more likely to adopt telemonitoring.

Ease-of-use. Telemonitoring systems cannot be intrusive or difficult to use, or

the patients will not adhere to the recommended usage. A European study examined the

effectiveness of using mobile phone technology for home monitoring and subsequent

54

reductions in readmissions (Scherr et al., 2009). The study included 120 patients. Of the

66 randomized to the telemonitoring care group, 12 were unable to participate because

they could not operate the mobile phone. The researchers concluded that finding an

appropriate user interface for elderly patients for data acquisition and transmission is an

essential factor for the successful adoption of telemonitoring.

A limitation of telemonitoring has been how well telemonitoring integrates with

the life of a patient (Elwyn et al., 2011). Technology can be physically awkward and

confusing to a patient. The Chaudhry (2010) study included 1,653 CHF patients who

were monitored over a six-month period and followed for two years. The design of the

study was completed several years before the final study results were published

(Chaudhry, 2007). During that period, new technology was evolving, such as

CardioNet’s Mobile Cardiac Outpatient Telemetry (MCOT™), that does not require

significant patient interaction and may potentially achieve higher compliance (Saarel et

al., 2008). The (Vasamreddy et al., 2006) CardioNet MCOT™ study involving patients

who were undergoing radiofrequency catheter ablation is unrelated to CHF but is relevant

with regard to telemonitoring patient compliance. The study was small, with 19 patients,

but the results showed that patients must be comfortable with telemonitoring technology

before they will embrace it as part of their care. Ten patients complied completely with

monitoring requirements. Of the nine patients who did not comply, six gave no reason,

one complained of monitor beeps, one had moved to a location where CardioNet could

not provide support, and one complained of skin irritation from the sensors. After the AF

ablation, the protocol stated patients would wear the monitor for 1 week every month for

6 months. This may have been too aggressive a monitoring protocol. The data about

55

compliance is inconclusive, but there was no mention of complexity or requirements for

patient interaction.

A large multicenter trial designed to evaluate the potential of telemonitoring to

reduce hospital readmissions of heart failure patients found that 14% of patients in the

telemonitoring care group did not use the interactive voice response monitoring system at

all, and 45% who used the system lost interest and failed to comply over time (Chaudhry

et al., 2010). Asch, Muller, and Volpp (2012) said that ideally, telemonitoring should be

welcomed, but making technology user-friendly is a key requirement to achieving

successful adoption. Their study showed that 14% of the participants would not use the

IVR and nearly half gave up before the study was completed.

Horton (2008) performed a qualitative study on use of fall detectors and bed

occupancy sensors to reduce the fear of falling among elderly participants. The major

problem observed by the participants was a significant number of false positives. In

addition to a feeling of being monitored in their home, participants were concerned the

false alarms would place a burden on family members or caregivers. Interview

comments that were relevant to the CTH CHF telemonitoring study were that the

monitoring devices were a nuisance. Such comments validate the importance of minimal

patient interaction with the telemonitoring technology.

Kataoka (2009) described a method of measuring body fluid levels as a by-

product of obtaining the patient weight. The scale uses a bio-impedance technique in the

scale to measure impedance of an electrical signal that passes from the scale and through

the body. A lower impedance correlates to a higher level of fluid is in the body.

Techniques such as this are becoming adopted by leading edge consumers using weight

56

scales such as the FitBit Aria Wi-Fi scale (Tucker, 2009). The concept that FitBit is

advocating is for active consumers to continually use the FitBit pedometer and weight

scale to automatically upload their activities to a Web portal where the information can

be reviewed and shared with others. As observed by Kataoka (2009), the bio-impedance

measuring technology offers potential benefits in the monitoring of CHF patients, but it

has the dependency that the patient has to remember to go to their scale on a consistent

basis to allow for the data capture.

Other studies have found a more promising embrace of telemonitoring. In a study

including 43 patients using the Health Buddy® device conducted by Karg (2012), 100%

of the patients met the established compliance target of use on two-thirds of the days for

which technical support was available. Survey questionnaires indicated that the patients

trusted the telemonitoring technique and security of the data collected and were satisfied

with the technical aspects of how the device operated. The patients were comfortable

communicating with the physician using telemedicine.

A European study set out to determine the effectiveness of using mobile phone

technology for home monitoring and subsequent reductions in readmissions (Scherr et al.,

2009). The study included 120 patients. Of the 66 randomized to the telemonitoring care

group, 12 were unable to participate because they could not operate the mobile phone.

The researchers concluded that finding an appropriate user interface for elderly patients

for data acquisition and transmission is a significant challenge for research studies of this

nature. Pare, Moqadem, Pineau, & St-Hilaire (2010) examined 62 studies where

telemonitoring was used for various chronic diseases including hypertension, heart

failure, asthma, and diabetes. The researchers found a wide range of patient abilities to

57

interact with technology. They suggested that direct monitoring with minimal patient

involvement might be promising.

Lack of demonstrated value. A major challenge to the successful adoption of

telemonitoring is the lack of clear evidence that telemonitoring makes a positive

difference in patient outcomes. Demonstrated and documented value is a key factor in

gaining momentum toward widespread adoption. Although the studies highlighted in the

Benefits of Technology section do show positive results, such outcomes are not

consistent. A European systematic review of 62 studies of telemonitoring with multiple

chronic diseases found that for heart failure, telemonitoring had no statistically significant

difference in hospital readmission rates (Pare et al., 2010). A systematic review of 10

studies in Germany concluded that there is no evidence of the benefits of telemonitoring

compared with normal care (Augustin & Henschke, 2012).

A large study funded by the National Heart, Lung, and Blood Institute (People

Science Health, 2012) and supported by Yale University, performed a randomized

controlled experiment with more than 1,600 patients (Chaudhry et al., 2010). The study

concluded that telemonitoring had no significant effect on the readmission rates of the

patients. The study used interactive voice response (IVR) technology where the patient

was required to provide input on his or her condition. The expectation was that patients

would call in the IVR system six times per week. Adherence was defined as 3 calls per

week. Approximately 85% of patients in the telemonitoring care group made at least one

call per week. Of those calls, 90% fulfilled the intervention, which was the delivery of a

set of data to the caregivers. The adherence dropped from 90% during the first week of

the study to 55% by the 26th week. The study design called for minimal interaction

58

between the patient and the caregivers, unless suggested by the IVR intervention.

Twenty-one percent of patients in the study did not complete the final IVR interview.

The adherence of this study suggests that telemonitoring that is dependent on the patient

for proactively providing daily data may not be the best use of technology

Other concerns about the value of telemonitoring relate to the cost of the

telemonitoring and the time requirement on physicians to interpret the data. Seto et al.

(2012) suggested that telemonitoring used with heart failure patients has shown

inconsistent findings, because of the wide range of interventions resulting from the

monitoring and due to a wide diversity of study designs. To gauge the perceptions of

mobile phone-based telemonitoring, the researchers interviewed 22 telemonitoring

patients and 5 clinicians. The perceptions were positive; patients were more informed

and confident and clinicians had real-time data about the condition of their patients. Both

patients and clinicians expressed concern about the long-term cost of the telemonitoring

program. Clinicians expressed concern about the time commitment to be in a real-time

management mode with patients.

One source of value for patients is their HRQL. Researchers in Toronto, Canada

performed a randomized controlled experiment (RCE) with 100 CHF patients. The 50

patients in the telemonitoring care group entered their weight, blood pressure daily, and

data from a single-lead ECG weekly. The study results showed that patients on

telemonitoring experienced an improved quality of life. This was attributed to improved

self-care made possible by information received on their mobile phones. The

involvement of the clinical support team also contributed to the improved quality of life.

Although the primary endpoint of hospital utilization showed no difference between the

59

control group and the telemonitoring care group, the patients in the study gained value

through their improved HRQL.

Cusack et al. (2008) performed an extensive search for evidence of value from a

broad-based use of telehealth. A simulation based on the research findings predicted

savings of more than four billion dollars per year if telehealth systems were installed in

nursing homes, prisons, emergency departments, and physician offices across the United

States. The greatest savings were from a reduction in the cost of face-to-face office

visits. The authors recommended that policymakers and providers remove the barriers to

telehealth adoption so that it could be made available to all. One area of significant value

may be the use of telemonitoring as a tool to reduce hospital readmissions.

There may be other areas of potential value from telemonitoring that are related to

reduction in readmissions but not dependent on the reductions. For example, physicians

may gain value by using telemonitoring to get data that enables them to prescribe

medications more accurately. Antonicelli et al. (2010) found that although beta-blockers

have the potential to improve outcomes for CHF patients, physicians often do not

prescribe the drugs because of concerns about dangerous side-effects. The Antonicelli et

al. (2010) study results indicated that the use of beta-blockers in the telemonitoring arm

of an RCE was more consistent and at the proper dosage than in the control arm of the

study. This result can add value to physician service delivery and to the outcomes for the

patients.

The future of telemonitoring. As new technology becomes smarter, faster, less

expensive, and more consumer friendly, it is likely that significant advances will accrue

to telemonitoring. The Abramson Group has received Federal funding to pursue the Blue

60

Box Project (Abramson, 2012). The Blue Box Project has developed a new wireless

technology device for use by patients that can collect data about multiple physiological

factors, facilitate self-help, enable direct communication with health care providers, and

provide guided interventions for caregivers. Initial studies of the device with a small

number of patients has shown good validity of various cardiac measurements (Pollonini,

Rajan, Xu, Madala, & Dacso, 2012).

One of the challenges of telemonitoring, in the hospital or in a residence, is wiring

(Fong, Fong, & Li, 2011). Wires can be annoying, intrusive, and present the risk of being

connected improperly. Jain (2011) described new standards for a wireless body area

network (WBAN), also known as a medical body area network (MBAN), that can gather

data from multiple sensors attached to the body in various ways and transmit the

information to health care providers designated by the consumer. The Federal

Communications Commission (FCC) unanimously approved a plan to allocate spectrum

for MBANs.

Consumer technology such as the FitBit may lead the way toward advancements

in health care technology. Attendees at the International Consumer Electronics Show in

Las Vegas in January 2012 saw more than 200 electronic communications technologies

in the Digital Health and Fitness category (ConsumerElectronicsShow, 2012). As the

technology becomes more intelligent, less costly, and simpler to use, it will also become

more pervasive.

Telemonitoring summary. Telemonitoring has the potential to reduce the cost

of health care, improve quality of life for patients, extend health care across the

continuum of care, and make health care available in rural areas with limited health care

61

resources. Despite the potential, the adoption of telemonitoring has stalled (Zanaboni &

Wootton, 2012). Achieving successful and widespread adoption of telemonitoring will

require concerted efforts by policymakers and administrators to remove regulatory and

structural inhibitors, identify and deploy easy-to-use technologies, provide training, and

consider financial incentives for appropriate use. More studies are needed to identify the

financial and clinical benefits that can be directly linked to telemonitoring as an

independent variable.

Relationship of Heart Activity and CHF Readmissions

Despite advances in pharmacological and non-pharmacological treatments for

CHF over the past 15 years, the prognosis remains poor (Cowie & Davidson, 2012).

Although the literature search has not yielded any studies linking a sole change in heart

activity to an impending deterioration leading to a hospital readmission, a major study

confirmed that a high heart rate correlates with increased risk of death from heart failure

(Bohm et al., 2010). The heart failure treatment study showed that patients with a heart

rate greater than 87 had twice the risk for heart failure hospitalization. Most studies, like

that by Cowie and Davidson (2012), include monitoring of weight and blood pressure,

which both require significant patient interaction. The goal of the CTH study is to

minimize patient involvement as a strategy to gain high adherence to the monitoring

protocol.

Lieback et al. (2012) performed a telemonitoring study that combined patient-

measured blood pressure and body weight with remote transmission of data from

implantable cardioverter-defibrillators (ICDs). An ICD is a garage door opener-sized

device that is implanted in a patient’s chest. The ICD can detect and stop an unusual and

62

life-threatening heart beat, and is used for patients that have a serious condition that puts

them at risk of cardiac arrest. The primary goal of the study was to investigate if there is a

correlation between the weight and blood pressure measurements and the heart rate data

from the ICDs. The sample size was 32 and 4,000 pairs of data were collected each day.

The study showed that weight correlated with higher heart rates. Blood pressure showed

a non-significant correlation. Although the normal purpose of the ICD is preventive,

Lieback et al. (2012) believe, based on the data they examined, that telemonitoring data

collected from an ICD could form the basis of an algorithm that could predict impending

deterioration of CHF. A long-standing issue for patients with implantable devices is

psychological adjustment and acceptance by the patient (Burns, Serber, Keim, & Sears,

2005).

Singh et al (2012) believe that telemonitoring to predict heart failure

deteriorations has significant potential to improve outcomes for CHF patients. The

researchers conducted a review of five implantable heart-monitoring devices. There was

only one major trial of any of the devices. The CardioMEMS heart failure sensor

resulted in significantly reduced CHF hospitalizations. They said that more large RCEs

are needed to determine if the devices are effective.

Whellan et al. (2010) conducted a prospective, multicenter observational study

from which they concluded that it is more effective to monitor multiple aspects of a

patient’s physiology than monitoring a single parameter. They predicted that new

devices would be developed in the future that have multiple technologies built into a

single sensor. The majority of CHF hospitalizations are related to increased congestion

due to fluid build-up, and many physicians prescribe medications to reduce the excess

63

fluid in the body (Singh et al., 2012). The patient reports the symptoms that cause the

physician to prescribe pharmacological treatment. Singh et al. (2012) said that recent

studies show that implantable cardioverter-defibrillators, such as previously described, or

even more invasive devices that can monitor activities in the interior of the heart

combined with telemonitoring may be well suited to detect impending episodes of heart

failure.

The expanded use of advanced telemonitoring technology will require more

studies to determine which type of patient is most likely to benefit, what parameters

should be monitored, what telemonitoring data should be captured, and how

telemonitoring data should be analyzed and converted into actionable interventions.

Monitoring based on patient symptoms such as weight and blood pressure may not

provide enough warning before an intervention is called for. The CTH study detected

irregularities of a patient’s heart beats using a more patient-friendly approach than

implantable devices or devices requiring significant patient involvement. Researchers and

clinicians obtained data on the activity of the patient’s heart as an alternate method to

produce actionable information for interventions with CHF patients.

Recruitment

Many of the research studies reviewed described participant recruitment as a

challenging aspect of the research process. Kibby (2011) said that more than 80% of

patients eligible for a clinical research study say they will participate, but only 10%

actually do. Success in recruitment can vary widely depending on the specifics of the

population and how they are invited to participate. Tompkins and Orwat (2010)

conducted a home telehealth study of seniors covered by Aetna Healthcare in New York,

64

New Jersey, and Pennsylvania. The Aetna members who qualified were 2,314. Because

of insurance coverage changes, 114 members were excluded, leaving 2,200 who were

randomized. The final number of participants in the study was 316 (14.4%). One of the

challenges in large studies where members are contacted by mail is that many, in this

case 563, cannot be reached. The actual number who declined to participate was 578

(26.3%).

McHenry et al. (2012) said that there are four themes that are critical to effective

recruitment. First is to select a population appropriate to the study. Effective

communication and building trust between the researcher and the patient is essential.

Offering security and comfort supplement the communication. Finally, it is important for

the researcher to offer thanks to the potential participant. The recruitment effort for a

study of older adults yielded 72% participation (McHenry et al., 2012). Whitten and

Mickus (2007) studied home telecare for CHF patients and conducted a personal

interview of the participants. Ninety-six percent said that they had no concerns about

participating in the study. Blanton et al. (2006) said that a well-designed recruitment

strategy for a research study is as important as a well-designed research design. In a

study of extremity constraint-induced therapy evaluation (EXCITE), Blanton et al. (2006)

followed a comprehensive process to ensure the best possible recruitment. They were

able to recruit 222 participants out of a target population of 240 (93%).

An important factor in recruitment success is building a relationship of trust

through face-to-face meetings with patients (McHenry et al., 2012). This can be difficult

to achieve with consistency in multi-site studies. Optimum recruitment depends on

identifying the patients who are potential study participants. As the length of time from

65

patient admission to discharge decreases, and multiple providers in the hospital see

patients, identifying a patient for a study can be difficult. McHenry et al. (2012)

suggested that a helpful consideration is offering some service associated with the study,

such as blood pressure checking.

Conclusion

Many reviews of telemonitoring focus on IVR patient input, telephone-based

monitoring where a nurse may call the patient and gather data about his or her condition,

or the traditional patient involvement to gather data on body weight, blood pressure, and

oxygenation level. Technological advances have lowered the cost and improved the ease

of use of telemonitoring. Electronic sensors can be attached directly to patients in a non-

intrusive manner and transmit data directly to health care providers. As the technology

becomes less expensive, less obtrusive, and requires less patient interaction, larger studies

can be performed to evaluate the effectiveness of the more advanced technologies.

Although the new technologies, such as CardioNet MCOT™, do not appear to have ease-

of-use or annoyance issues with patients, this needs to be validated in new studies.

Researchers have suggested that directly monitoring the activity of the heart may be an

earlier and more reliable predictor of impending deterioration of the condition of a CHF

patient. The CardioNet MCOT™ telemonitoring service provides continuous data about

the activity of the heart. The CTH study aims to investigate whether these data can be

predictive, provide actionable data to caregivers, and result in reduced hospital

readmissions.

The literature review provided valuable insight and perspective from the studies

conducted by researchers around the world. The review suggests that there are gaps in

66

the research that further research needs to fill. First is to address the more advanced

technology, compared to traditional body weight scales, etc., which is now available.

Researchers need to delineate the relative effects between the use of telemonitoring and

the use of person-to-person interventions. The review provides clues for the design of

future research and was important input to the design of the OR study.

67

Chapter 3

Methodology

The purpose of the OR study was to determine through statistical analysis if there

was a significant difference in hospital readmissions because of using in-home

telemonitoring of CHF patients. The source of the data for the OR study was an archive

of anonymized secondary data from a CTH cardiac telemonitoring study that commenced

in March 2013. A readmission was counted if it occurred within 30 days of discharge

from the hospital. All 30-day readmissions were counted regardless of the reason for the

readmission. This is consistent with the way CMS counts readmissions. The UCG

received usual medical care. The TCG received usual care plus telemonitoring using the

CardioNet MCOT™ service. Usual care included a risk assessment, a personalized

discharge plan developed by a nurse navigator (NN), and an appointment for the patient

to see their PCP within seven days of discharge.

The independent variable in the study was the use of the CardioNet MCOT™

service. The CTH and CardioNet provided the equipment and telemonitoring service for

all patients in the TCG. The primary dependent variable was the 30-day all-cause

readmission rate. The other dependent variables of interest include the number of

interventions by type (medication changes, visits by a nurse, visits to a PCP, visits to a

specialist, round-trip visits to the ED, calls to an EMS, or no action taken). The archival

data includes demographic information, baseline characteristics of the population sample,

and data related to readmissions and interventions. The archive contains detailed clinical

data, but that was not used in the OR study. The archive does not contain any personally

identifiable information. The data archive is described in the Data Collection section.

68

Chapter 1 provided an introduction and chapter 2 provided a review of the relevant

literature. Chapter 3 provides a description of the research design and methodology for

the OR telemonitoring study.

Research Method and Design Appropriateness

The OR study used quantitative methods to investigate the relationship between

telemonitoring and hospital readmissions and the number and type of interventions.

Research question: How effective is home-based telemonitoring in providing actionable

data to care providers that can result in reduced hospital readmissions for patients with

CHF compared with the UCG? A related question of interest is the number of

interventions by type (medication changes, visits by a nurse, visits to a PCP, visits to a

specialist, round-trip visits to the ED, calls to an EMS, or no action taken). The

CardioNet MCOT™ service can detect abnormalities in heart activity. The question is

whether the data and alerts from CardioNet can help predict an impending problem that a

cardiologist can address, in lieu of EMS followed by hospital readmission. The primary

measure of the effectiveness of these actions is the 30-day all-cause readmission rate of

patients.

Research Questions and Hypotheses

Ho1: The null hypothesis is that there is no difference in CHF patient

readmissions to the hospital between the TCG and the UCG.

Ha1: The alternative hypothesis is that there would be a significant difference in

the number of readmissions in the TCG.

H02: The null hypothesis is that there is no significant difference in the number

or type of interventions between the TCG and the UCG.

69

Ha2: The alternative hypothesis is that there is a significant difference in the

number and type of interventions between the TCG and the UCG.

Method Appropriate to Purpose

OR designs are quantitative but are not experimental. Unlike experimental

designs, the observational researcher does not manipulate the independent variable and

observe the effect on dependent variables (Fitzpatrick & Wallace, 2006). Since the OR

study did not include manipulation of any variables nor have access to any primary data,

the observational design was well suited. The observational design method is to

retrospectively examine anonymized archival data and investigate whether there are

statistically significant relationships among the variables. See Figure 5 for a diagram of

the process that was used to analyze the archival data from the CT study. Although cause

and effect cannot be determined with an observational design, the design can identify

relationships among variables and can be useful in suggesting additional hypotheses

(Mann, 2003).

Usual care includes a combination of pharmacological treatment and visits by

various caregivers. The care plan begins when a physician makes a risk assessment while

the patient is in the hospital. A NN developed a personalized discharge plan for each

patient that includes an appointment for the patient to see his or her PCP within seven

days of discharge. The elements of usual care were provided to all patients in the UCG

and the TCG.

70

Figure 5. Observational Research Design

71

Focus of the Design

The focus of the design is the patient and whether the research hypothesis could

reduce his or her readmissions to the hospital. The primary endpoint of the study was the

number of 30-day all-cause hospital readmissions. Secondary dependent variables that

were measured included the number of interventions by type (medication changes, visits

by a nurse, visits to a PCP, visits to a specialist, round-trip visits to the ED, calls to an

EMS, or no action taken). The primary independent variable is the use of CardioNet

MCOT™ telemonitoring.

Research Questions

Research question: How effective is home-based telemonitoring in providing

actionable data to care providers that can result in reduced hospital readmissions for

patients with CHF compared with usual care? The related question of interest was about

the number and type of interventions. The question about CardioNet MCOT™ was

whether the service would provide actionable data that could enable a provider to make

an intervention for the patient that eliminates the need for a hospital readmission.

Population and Sample

CTH, located in New England, serves a market of between 500,000 and 1,000,000

people and is the primary provider of healthcare for approximately 250,000 people in its

service area. A patient with CHF typically presents to the ED with shortness of breath or

dizziness. After being stabilized, the patient is usually admitted to the hospital. After

examination by an attending physician, the patient is given a primary diagnosis, which is

then used for statistical, reporting, and reimbursement purposes. When comorbidities

72

exist, a patient may have multiple diagnoses, but there is only one primary diagnosis.

During the 34-month period from September 2009 through June 2012, there were

approximately 1,500 patients admitted to CTH with a primary diagnosis of CHF. The

monthly admissions are relatively steady. The average for the reported period was 42.4

per month. See Figure 6 for a graph of CHF admissions.

Figure 6. CHF Patient Admissions from September 2009 through June 2012

CTH planned the population sample at a size large enough to provide statistical

power and external validity. Baseline characteristics of the population sample are

expressed as means and percentages, with comparisons being made between the two

groups using chi squared and independent samples t tests. Inferential statistics was used

to determine the significance of the difference in the primary dependent variable between

the TCG and the UCG. Where the probability level is .05 or lower, that establishes

statistical significance, that the difference is not due to chance, and that the hypothesis

0

10

20

30

40

50

60

Sep

-20

09

No

v-2

00

9

Jan

-20

10

Mar

-20

10

May

-20

10

Jul-

20

10

Sep

-20

10

No

v-2

01

0

Jan

-20

11

Mar

-20

11

May

-20

11

Jul-

20

11

Sep

-20

11

No

v-2

01

1

Jan

-20

12

Mar

-20

12

May

-20

12

CHF Admissions at CTH

73

should be rejected. A second pillar of inferential statistics is statistical power (Bausell &

Li, 2002).

Statistical Power

There are two parts to determining statistical power. First is a hypothesis of what

difference in the primary dependent variable could be expected between the TCG and the

UCG based on the theoretical context of the study. Second is the probability that the

results will be statistically significant if that hypothesis is not rejected. In effect,

statistical power provides a way to predict if the results of the study will be statistically

significant before the study is conducted. Such a prediction can be important to those

who provide funding or approval of a study because they may want to avoid a study that

is not adequate in size to be assured of statistical significance if the hypothesis is not

rejected (Bausell & Li, 2002).

For statistical planning, the OR study included the assumption that the TCG

would show a 45.4% reduction versus the UCG. For the period of September 2009 to

July 2012, the average readmission rate was 22%. Using the formula (σ = √ (p*(1-p))

yielded a σ of .414. The hypothesized reduction in the readmission rate would bring the

rate down from the 22% to 12%. The difference between the means of 22% for the UCG

and 12% for the TCG, divided by sigma yields an effect size of .241. Using the effect

size, t-test (two-tailed alpha = 0.05) corresponding to a 95% confidence interval, yields a

power of 80% to detect a statistically significant difference with a sample size of 130

patients. Calculations were made using G*Power (Erdfelder, Faul, Lang, & Buchner,

2007). The power calculation means that there is an 80% probability of incorrectly

74

failing to reject the null hypothesis that there is no difference in readmission rates

between the two groups when in fact a real difference exists.

Sixty-five patients per group were the required ingestion into the study of 130

patients over the course of the study. Patients older than 18 and with a primary diagnosis

of CHF were included in the study. Some patients were not suitable to invite to

participate in the study and some declined the invitation. Patients with impending

surgery, multiple comorbidities that made them too sick to participate, or those who did

not have the mental lucidity to interact with the telemonitoring equipment were excluded

from the study. It was estimated that 20% of admitted patients would be excluded. See

Table 2 for a complete list of reasons for which patients were excluded. The actual

numbers and reasons for exclusion are described in chapter 4.

Table 2

Patients Excluded

Patients on an alternative telemonitoring system

Patients discharged to hospice

Patients with scheduled surgery within 30 days

Patients with severe cognitive impairment

Patients with known multiple significant comorbidities:

assessed by admitting physician

Patients with allergic reactions to adhesives

Pregnant women

Prisoners

Patients who declined invitation

Assuming 42 patient admissions per month and a recruitment rate of 80%, 134

patients would have been eligible for the study, exceeding the desired study size of 130.

If the target of 130 patients had been reached before four months, CTH may have stopped

recruitment to conserve limited funding and staffing resources.

75

Recruitment

CTH’s strategy to obtain a high participation rate from eligible patients was to

identify potential patients early and communicate effectively. The CTH study took place

at a single site – the hospital. All CHF admissions occurred in the cardiac care unit on

the eighth floor of the hospital. Flat-panel displays showing all patients and their

diagnosis made it easy for the hospital to identify the candidates for recruitment. These

factors were expected to provide significant advantage over the challenge in multi-site

studies in identifying and locating prospective study participants.

I was not involved in recruiting patients for the CTH study. The CTH conducted

the recruitment and met with 100% of the potential participants in the population sample

to explain the purpose of the study and how it would be conducted. A documented

checklist and script were used to ensure that all legal and policy requirements were met

and that the patient understood all risks, benefits, and the option to discontinue

participation. The assumed rate of recruitment of the total patient population after

exclusions was 80%.

Informed Consent

Informed consent is a process that assured that the CTH research study met high

ethical standards (Pranati, 2010). The department of research at CTH informed eligible

patients of all aspects of the research. After the patients gained a comprehension of all

aspects of the study, they had an opportunity to express their willingness to participate.

76

All such approvals were documented following the established procedures of CTH. The

participants were fully informed and the decision to participate was autonomous.

Institutional Review Board

CTH received its IRB approval from the Biomedical Research Alliance of New

York (BRANY) on January 11, 2013. The CTH study produced an archive of

anonymized secondary. The University of Phoenix School of Advanced Studies IRB

gave approval to perform an OR study with the archive on April 10, 2013.

Confidentiality

Confidentiality of information a patient makes available to a health care provider

has been the basis of a trust relationship (Meystre, Friedlin, South, Shen, & Samore,

2010) that has existed for centuries based on the Hippocratic Oath (NLM, 2002). The

CTH study was conducted with that trust in mind and complied with the Health Insurance

Portability and Accountability Act (HIPAA), which protects the confidentiality of patient

data (USDHHSHIP, 2012), and the Common Rule, which protects the confidentiality of

research subjects (USDHHSFP, 2012).

All data collected about the patients at CTH were recorded in the hospital’s

electronic medical record (EMR) system and were not made available to anyone outside

of the hospital. The OR study used data from an archive created by CTH that contains

demographic and study results. The archive of secondary data were anonymized and

does not contain any personally identifiable contact or health care information.

Patients were randomized into the UCG and TCG using a randomization

technique so that the treatment for an individual patient would not be predictable. CTH

77

recruited patients one at a time from March to November 2013. CTH applied the

randomization process after a patient had been deemed suitable for the study and had

provided his or her informed consent. The data archive of secondary data included non-

identifiable participant data representing measurement of the variables associated with

each. The randomization provided in the data archive was intended to provide a sound

basis on which the null hypothesis could be tested (Fayers & Machin, 2010).

Instrumentation

Instrumentation refers to medical equipment and sensors that can measure or

monitor the physiological status of a patient. The CTH study used CardioNet MCOT™

equipment and service to gather heart-related data from patients. Since the time of the

Chaudhry el al. (2010) study that concluded telemonitoring has no effect on CHF

readmissions, new technologies have emerged that could change that conclusion. One

example is CardioNet, the provider of a technology called Mobile Cardiac Outpatient

Telemetry™ (MCOT™). CardioNet MCOT™ is the technology that served as the

telemonitoring technology for the CTH study. MCOT™ includes an at-home cardiac

monitoring service with real time analysis of the patient’s heartbeats, automatic detection

of arrhythmia, and wireless transmission of the ECG data.

CHF telemonitoring has traditionally been based on monitoring of patient weight,

blood pressure, and oxygenation (Blasco et al., 2012). Each of these measurements

requires significant diligence and involvement of the patient. MCOT™ can be safe and

easy to use, even for children, because it does not require significant patient interaction

and may achieve high compliance to the research design goals (Saarel et al., 2008). See

78

chapter 2 for more information about CardioNet and other telemonitoring literature. See

cardionet.com for more information about the CardioNet MCOT™ system.

I did not have access to any primary data collected by CTH and CardioNet. The

archive of secondary data include the number and types of interventions that resulted

from the CardioNet alerts and were the source of the data for the OR study.

Data Collection

Fayers and Machin (2010) said that the primary endpoint in every research study

should be explicitly defined. For the CTH study, the primary endpoint is the number of

30-day all-cause readmissions to the hospital. Secondary endpoints are also of great

interest. The data archive includes readmission data and data about the number and types

of interventions. The archive does not have any personally identifiable contact or health

care information.

The data archive. The data archive was a Google Drive folder containing three

Google Spreadsheet databases as described in Table 3. Access to the folder and

databases were password-protected. The archive did not contain any personally

identifiable data.

79

Table 3

Spreadsheet Databases Contained in the OR Study Data Archive

Spreadsheet Database Name Contents of the Database

Patient Tracking Database Date patient was assessed for inclusion in

study. Reason code if excluded.

Patient Intake Database Baseline characteristics of study

participants as described in Table 4.

Patient Follow-up Database Detailed data about readmissions,

interventions, and clinical data about heart

activity.

Patient tracking database. The patient tracking database was used to keep track

of the recruiting process. If a patient was excluded or dropped out after being included,

the database has a reason code. Although not a dependent variable, the tracking status of

patients provides insight about CHF patients that were invited to be participants in the

study.

Patient intake database. For each patient enrolled in the study, the patient intake

database includes the baseline characteristics of the patient. The baseline data include

gender, race/ethnicity, previous medical conditions, comorbid conditions, and

medications being taken (see Table 4 - Baseline Characteristics of Study Participants).

The baseline data contain no personally identifiable information.

80

Table 4

Baseline Characteristics of Study Participants

Characteristic

Demographics

Age

Gender

Race/ethnicity

Previous conditions

Acute myocardial infarction (AMI)

Coronary artery bypass surgery (CABG)

Percutaneous transluminal coronary angioplasty (PTCA)

Stroke

Ejection fraction (EF)

Comorbid conditions

Asthma

Chronic obstructive pulmonary disease (COPD)

Diabetes

Hypertension

Hyperlipidemia

Medications

Beta blockers

Cardiac glycosides

Angiotensin-converting-enzyme inhibitors (ACE)

Angiotensin receptor blockers (ARB)

Diuretics

Implanted Device

81

Patient follow-up database. The patient follow-up database contains all the data about

the primary and secondary dependent variables. The database includes data for each

study participant for each of the weeks during the 30-day of study.

The telemonitoring data collected daily from the patient shows trends and

provides e-mail alerts to caregivers if any measurement is outside of preset limits

established as part of the care plan for the patient. Caregivers were able to use these data

to care proactively for the patient. For example, if the data from telemonitoring showed a

sudden change in the patient’s heart rhythm, a cardiologist was able to make a change in

medications. In some cases, the data prompted phone follow-ups or home visits. The

objective of the care was to prevent the need for a hospital readmission. The

telemonitoring data are not part of the data archive, but the interventions that resulted

from the availability of the telemonitoring data are in the database.

A readmission due to a fall or other illness unrelated to CHF was considered a

readmission for purposes of the study. The primary endpoint was a hospital readmission

within 30 days, a key parameter measured by CMS (Mulvany, 2009). An important

secondary endpoint is the number or type of interventions.

The patient follow-up database includes all interventions that occurred during the

30-day period of telemonitoring whether by nurse, PCP, or specialist. The data include

the number of alerts received from or about patients, medication changes made by

physicians, and other interventions such as a visit to a PCP or cardiologist. The database

does not include any personally identifiable data. See Table 5 for a list of interventions

that were included.

82

Table 5

Intervention Measurements Included in Archive of Secondary Data

Type of Intervention

Medication changes

Visits by a nurse

Visits to a PCP

Visits to a cardiologist or other specialist

Round-trip visits to the ED

Data from the heart of the patient. The CardioNet MCOT™ monitor receives

basic heartbeat data and a heart rate trend chart showing high, low, and average heart

rates. Additional data are sent to CardioNet whenever a patient has a symptom and

presses a button on the monitor. The cardiology department established triggers for

various heart activities as part of the telemonitoring plan, and the monitor transmitted

these data whenever a trigger was activated. Although the clinical data collected by

CardioNet is of great interest to cardiologists, none of these data were in the archive of

secondary data used in the OR study. An area of interest in the OR study included what

interventions were taken as a result of the data that the cardiologists received and whether

there was a statistically significant difference in readmissions to the hospital between

patients who were monitored and those that were not.

Data reported by CardioNet. The data provided to the CardioNet monitoring

center form the basis for the cardiologists to be able to provide an intervention to patients

83

in the TCG. If the interventions were successful, there could be a significant reduction in

hospital readmissions compared to the patients in the UCG. That was the alternative

hypothesis of this study. The data from CardioNet was made available to authorized

caregivers in the form of six reports including a basic daily report that shows an hourly

trend graph of heart activity over a 24-hour period, an urgent/requested report, a basic

summary report, an arrhythmia reporting/indicators report, condensed daily reports, and

an enhanced end of service summary report. None of these reports were part of the data

archive.

Exits from the Study

Some patients decided to exit the CTH study for personal reasons. Some found

the electrodes to be irritating or annoying in some way. A patient could move to a

different geographical area or change their health care provider and leave the study.

Unfortunately, mortality could be a factor for some patients. A patient could chose to

leave the CTH study at any time, but no data already collected remains in the patient

follow-up database, nor is his or her data included in any analysis in the OR study.

Validity and Reliability

Validity and reliability are two major properties of a research study associated

with good measurement (Christensen et al., 2011). The validity of research results refers

to the degree to which the results measure what was intended (Kerr, Knox, Robertson,

Stewart, & Watson, 2008). A key tool to eliminate issues that might affect validity is

randomization. Because CTH used a digital computer with professional software to

assign the recruited patients to either the UCG or the TCG, the different characteristics of

patients should be evenly distributed. For example, each group should be equally likely

84

to contain men or women, old or young, having multiple comorbidities, mental faculties,

technical dexterity, etc. As described in the randomization section, the randomizing

process was designed to shield the allocation of patients to the two groups so that no

inappropriate influence could take place.

The telemonitoring of heart activity used the CardioNet MCOT™ equipment and

service. There are no studies to confirm the reliability of CardioNet results, but a

company-sponsored study showed that an arrhythmia was either excluded or confirmed

as a primary reason for the symptom in 88% of CardioNet patients compared to 75% for

patients using the alternative older form of monitoring (Rothman et al., 2007).

Data Analysis

The primary endpoint of the OR study was the number of 30-day all-cause

readmissions. This is a completely valid and reliable measurement. All patients in the

study had been discharged from the hospital. When a patient from either the TCG or

UCG was readmitted to the hospital within 30 days, an attending physician or nurse made

an entry in the patient’s EMR. Such entries constitute the official source for measuring

of the primary dependent variable. Admissions data from the EMR were placed into the

archive of secondary data. No personally identifiable information was included. The

percentage of readmissions in each group was computed by dividing the net number of

patients in each group (after deducting any who exited the study) into the number in the

group who were readmitted.

The data archive includes data about the study participants, readmissions, and the

number and type of interventions. No personally identifiable information is included. The

spreadsheet databases from the archive were used to make statistical comparisons

85

between the TCG and UCG. A comparison was made between demographic and other

baseline factors (see Table 3) for the two groups to look for any unexpected differences

between them. This comparison was made using chi squared and independent samples t-

tests.

For each intervention value in Table 5, the null hypothesis states that there is no

difference between the number of interventions that occurred in the TCG compared to the

number in the UCG. The μ and σ were calculated for each type of intervention, and from

them the probability (p value) was calculated and compared to α = .05 (Christensen et al.,

2011). For an intervention having a p < α, the null hypothesis was rejected and it was

concluded that there is a significant difference between the number of interventions in the

TCG versus the UCG. Where p > α, the null hypothesis was accepted because the

probability of the difference being due to chance is less than 5%.

I considered bootstrapping as a re-sampling method to supplement the traditional

statistical analysis. However, a review of the literature about studies that used

bootstrapping, disclosed none that were similar to the telemonitoring study. Hence,

bootstrapping was not used as part of the statistical analysis.

Conclusions

CHF is a chronic affliction that affects millions of Americans, imposes a

significant burden on the health care system, and causes patients to have a reduced

quality of life. One in five CHF patients discharged from the hospital are readmitted

within 30 days, causing further reduction in HRQL for the patient and their families.

Pharmacological and non-pharmacological advances have improved the quality of life

86

somewhat, but the prognosis is still not good. Outpatient care from PCPs and specialists

can help prevent readmissions, but frequent visits to these caregivers are costly.

The number of people requiring medical care because of the ACA is increasing,

and health care resources may become scarcer. Studies have shown that telemonitoring

can have a positive impact on the situation by predicting that a deterioration may be

imminent and alerting a caregiver to intervene, thereby obviating the need for

rehospitalization.

A relatively new technology from CardioNet provides a non-intrusive method of

gathering data about a patient’s heart activity with minimal patient involvement.

Although no research was found that showed heart activity was a predictor of impending

deterioration, studies have shown that there is a relationship between heart rate and

mortality from heart failure. The CardioNet MCOT™ service provided data about heart

rate and rhythm that may prove to be a timely predictor to allow a cardiologist to

intervene and prevent a readmission. If telemonitoring could result in significantly lower

readmission rates, patients could have improved quality of life, hospitals would be able to

conserve scarce facilities and resources, and the health care system at large could become

more effective and efficient.

87

Chapter 4

Results

The purpose of this OR study was to determine whether any significant

differences in hospital readmissions or in the number and type of interventions existed as

a result of the application of home telemonitoring. Congestive heart failure (CHF)

patients who were discharged from a community teaching hospital (CTH) in New

England comprised the sample. A total of 344 patients, discharged between March and

November of 2013, were assessed for inclusion in a cardiac telemonitoring study

conducted by the CTH and were included in a secondary data set. Discharged patients

were randomized into a telemonitoring care group (TCG) and a usual care group (UCG).

Statistical analysis was used to look for differences in the endpoints between the TCG

and the UCG using a subset of the data collected by CTH.

The purpose of chapter 4 is to present the research findings. The research

includes a description of the recruitment process that led to the formation of the TCG and

UCG and the data collection that led to the formation of the archive of secondary data.

These are followed by a descriptive statistical analysis of the baseline characteristics of

the patients in both the TCG and UCG. Finally, the chapter includes the data analysis

used to examine the primary and secondary dependent variables, which were the number

of 30-day all-cause readmissions and the number and type of interventions, and their

relationship with the independent variable, which was the application of CardioNet

telemonitoring. The intervention variables included the number of medication changes,

the number of interventions by a nurse, primary care physician, or cardiologist, and the

number of round-trips to the ED. Hypothesis testing, using independent samples t-tests,

was used to determine if the relationships were statistically significant and whether the

88

null hypotheses were rejected or were not rejected. Research question: How effective is

home-based telemonitoring in providing actionable data to care providers that can result

in reduced hospital readmissions for patients with CHF compared with the UCG?

Recruitment

The source of data were a study conducted by the CTH. The intent of the hospital

was to recruit a randomly selected sample of 130 patients. Between March and

November 2013, the hospital assessed 344 patients for inclusion in its study. All patients

over the age of 18 with a primary diagnosis of CHF were candidates for the hospital to

enroll in its study.

Exclusions

The hospital excluded 288 (84%) of the candidates from enrollment. Two

candidates were already on an alternative telemonitoring program, 45 were discharged to

hospice care or some other form of alternative care, 12 had a malignancy or were

scheduled for surgery, 113 had significant cognitive impairment or lack of ability to be

compliant in a study, 15 had multiple significant comorbidities, six were allergic to

adhesives, and 95 were unable to be recruited for various scheduling reasons before they

were discharged. Thirty additional candidates (8.7%) declined to participate in the study.

The remaining 26 patients were randomized into the TCG and UCG with 13 in each

group. See Figure 7 for a pictorial of the recruitment and randomization process.

89

Figure 7. Recruitment and Randomization

90

Randomization

CTH applied the randomization process described in chapter 3 after a patient had

been deemed suitable for the study and had provided his or her informed consent.

Twenty-six patients were included in the CTH study. The data archive of secondary data

included non-identifiable data about the 26 participants representing baseline

characteristics and measurement of the independent and dependent variables. The

randomization provided in the data archive was intended to provide a sound basis on

which the null hypothesis could be tested (Fayers & Machin, 2010).

Statistical analysis, described later in this chapter, confirmed that there were no

significant differences between the baseline characteristics of the participants in the TCG

compared to the UCG. The assumptions behind the use of Pearson’s chi-squared test

were met. Data were drawn from a random sample and the sample was drawn from a

population with a known and uniform distribution.

Attrition

The study period for each patient was planned to be 30 days from the date of

discharge from the hospital. During the course of the 30 days, seven patients from the

TCG and three from the UCG withdrew from the study. Three patients found the

CardioNet monitor to be overwhelming in complexity, one patient had a reaction to the

CardioNet sensors attached to his or her skin, three changed residency to a nursing home

that was not able to support the telemonitoring, and three decided to drop out for personal

reasons. The remaining number of patients for whom data were included in the data

archive for the OR study was six in the TCG and 10 in the UCG. See Figure 8 for a

pictorial showing the assignment of patients to the telemonitoring and usual care groups.

91

Figure 8. Assignment to Telemonitoring and Usual Care Groups

92

Sample Demographics and Characteristics

The data archive used for the OR study included descriptive data about the patient

participants. No personally identifiable data were included. The data were imported

from the patient intake database to IBM SPSS Statistics Version 21 (SPSS) for statistical

analysis. A p-value of .05 was used to guide interpretations of statistical significance.

The first set of baseline characteristics data included categorical data from the

history of the patient, such as gender, race, whether or not the patient had a previous

condition such as acute myocardial infarction (AMI), coronary artery bypass surgery

(CABG), percutaneous trans-luminal coronary angioplasty (PTCA), or stroke, and

whether or not the patient had an implanted cardiac device. Using SPSS, a Pearson chi-

squared analysis confirmed that there was no significant difference in the categorical data

between the TCG and UCG. See Table 6 for a summary of the categorical data from

baseline characteristics.

93

Table 6

Summary of Categorical Data from Baseline Characteristics

Characteristic TCG UCG Total P Value

Gender

Female 2 6 8

Male 4 4 8

Total 6 10 16 .30

Race

African American 2 2 4

White 4 8 12

Total 6 10 16 .55

Previoius AMI 1 3 4 .55

Previous CABG 1 0 1 .18

Previous PTCA 0 1 1 .42

Previous Stroke 1 2 3 .87

Implanted Device 0 4 4 .07

The second kind of data in the archive is a set of baseline characteristics including

discrete data such as age, ejection fraction (EF), which is a measure of how well the heart

is performing (Hsich & Wilikoff, 2013), the number of medications being taken, and the

number of comorbidities. An independent samples t-test was conducted to examine

whether there was a significant difference between the TCG and UCG in relation to their

age, EF, medications taken, and comorbidities. The comorbidities included were asthma,

94

chronic obstructive pulmonary disease (COPD), diabetes, hypertension, and

hyperlipidemia.

CHF can afflict patients at any age, including children, but it is most prevalent in

those over the age of 65 (Farcaş & Năstasă, 2011). Patients enrolled in the study had

ages ranging from 43 to 94. The test revealed no statistically significant difference

between the patient age in the TCG and UCG (t = -.42, df = 24, p = .68). The mean age

of the patients in the TCG was (M = 65.49, SD = 15.88). The mean age of the patients in

the UCG was (M = 73.44, SD = 13.19).

Pharmacologic therapy plays an important role in the routine treatment of elderly

CHF patients (Henriques, Costa, & Cabrita, 2012). The discrete data about medications

in the baseline characteristics of patients in the study considered only five categories of

drugs that are specific to the treatment of CHF. These categories included beta-blockers,

cardiac glycosides, angiotensin-converting-enzyme inhibitors (ACE), angiotensin

receptor blockers (ARB), and diuretics.

The t-test revealed no statistically significant difference between patients in the

TCG and UCG for the number of medications taken (t = .25, df = 24, p = .80). The mean

number of medications being taken by patients in the TCG was (M = 2.83, SD = .75).

The mean number of medications being taken by patients in the UCG was (M = 2.70, SD

= .67).

The EF is a measure of the performance of the heart and refers to the percentage

of blood that a person’s heart can pump out of a filled ventricle with each heartbeat. The

EF is typically .55 for a healthy person (Grogan, 2013). The t-test revealed a statistically

significant difference between the EF in the TCG and UCG (t = -2.26, df = 14, p = .04).

95

The mean EF in the TCG was (M = .27, SD = .14). The mean ejection fraction of patients

in the UCG was (M = .47, SD = .18). The t-test confirms that the patients in the TCG had

weaker hearts than patients in the UCG.

One of the challenges in treating CHF patients is that they often have multiple

concomitant diseases in addition to heart failure (Murad & Kitzman, 2012). The number

of the most prevalent comorbidities of each patient was collected from his or her EMR as

part of the baseline characteristics. The comorbidities included were asthma, chronic

obstructive pulmonary disease (COPD), diabetes, hypertension, and hyperlipidemia.

The t-test revealed a statistically significant difference between the comorbidities

in the TCG and UCG (t = -2.68, df = 14, p = .02). The mean comorbidity in patients in

the TCG was (M = 1.8, SD = 1.17). The mean comorbidity in patients in the UCG was

(M = 3.10, SD = .74). See Table 7 for a summary of the discrete baseline characteristics.

Although the patients in the TCG had weaker hearts, the patients in the UCG had

significantly more concomitant diseases along with their heart failure.

Table 7

Summary Of The Discrete Baseline Characteristics

Characteristic Group N Mean Std. Deviation P Value

Age TCG 6 65.49 15.88

.30 UCG 10 73.44 13.19

Ejection

Fraction

TCG 6 .27 .14 .04

UCG 10 .47 .19

Comorbidities TCG 6 1.80 1.17

.02 UCG 10 3.10 .74

Medications TCG 6 2.83 .75

.72 UCG 10 2.70 .67

96

Confounding Factors

The study commenced with 26 participants, including 10 who subsequently

withdrew from the study and, at that level (N = 26), t-tests showed that there was no

statistically significant difference in the ejection fraction (p = .16) or comorbidities (p =

.10) between the TCG and UCG. See Table 8 for a summary of the discrete baseline

characteristics including the withdrawals. The lack of significant difference between any

of the four discrete variables in the two groups indicates that the randomization was

effective. However, the attrition of 10 subjects resulted in the aforementioned variances.

Such variances could produce confounding effects in the relationship between the

independent variable (telemonitoring) and the primary and secondary dependent variables

(Christensen et al., 2011).

Table 8

Summary Of The Discrete Baseline Characteristics Including Withdrawals

Characteristic Group N Mean Std. Deviation P Value

Age TCG 13 74.57 14.62

.68 UCG 13 76.88 13.33

Ejection

Fraction

TCG 12 .34 .18 .16

UCG 12 .45 .19

Comorbidities TCG 13 2.23 1.17

.10 UCG 13 2.92 .86

Medications TCG 13 2.54 .78

.80 UCG 13 2.46 .78

Note. For ejection fraction, N = 24 because two patients had missing data.

Variables

The independent variable in the study is the application of the CardioNet

telemonitoring service. Those patients in the TCG received the CardioNet service for

thirty days from their discharge from the hospital, and those patients in the UCG did not.

97

Both groups received usual care. The primary dependent variable was the number of 30-

day all-cause readmissions to the hospital. The secondary variables included the number

of medication changes, the number of interventions by a nurse, primary care physician, or

cardiologist, and the number of round-trips to the ED.

Data Analysis

The data archive contained measurement data for each patient for each week of

the 30-day study. The data were summarized using Microsoft Excel and then imported to

SPSS for analysis. The independent samples t-test was used to determine if there was a

significant difference between the TCG and UCG in relation to the primary and

secondary dependent variables. The independent samples t-test produces two different

results, based on whether or not equal variances exist between the two groups. The

Levene’s test was used to determine which t-test result to use. For those cases where the

Levene’s test was not significant (p > .05), it was assumed that equal variances existed.

In those cases where Levene’s test was significant (p < .05), equal variances were not

assumed. The t-tests were performed using a confidence interval of 95% (p = .05). For t-

test results where p < .05, the test showed that there was a significant difference between

the two groups. Where p > .05, the test showed that the groups were not statistically

different.

The independent variable in the data analysis was the use of CardioNet

telemonitoring. The primary dependent variable was the number of 30-day all-cause

hospital readmissions. The secondary dependent variables were the number of

medication changes, the number of interventions by a nurse, primary care physician, or

cardiologist, and the number of round-trips to the ED.

98

Hospital Readmissions

The research hypothesis is that there would be a statistically significant difference

between the number of hospital readmissions for the TCG and UCG. The primary

dependent variable was the number of 30-day all-cause hospital readmissions. During

the 30-day periods for all of the patients, one hospital readmission occurred. A patient in

the UCG had a medical event, called a physician, was taken to the hospital, and was

admitted to the cardiac department. The admission was not the result of an alert from

CardioNet.

An independent samples t-test was conducted to examine whether there was a

significant difference between readmissions in the TCG versus the UCG. The test

revealed that there was no statistically significant difference between the numbers of

readmissions between groups (t = -.76, df = 14, p = .46).

Medication Changes

Physicians prescribe changes to the medications being taken by a patient based on

symptoms, laboratory tests, or data from cardiac monitoring. During the 30-day periods

for all of the patients, physicians ordered a total of 15 medication changes for 11 unique

patients: two changes for each of four patients and one change for each of seven patients.

Eight medication changes occurred in the TCG and seven occurred in the UCG. For

those patients in the TCG, physicians had the benefit of detailed clinical data from

CardioNet about the heart activity of the patients.

The research hypothesis is that the availability of the CardioNet data would result

in medication changes in the TCG that were significantly different than in the UCG. An

independent samples t-test was conducted to examine whether there was a significant

99

difference between medication changes in the TCG versus the UCG. The test revealed

that there was no statistically significant difference between the numbers of medication

changes between groups (t = 1.41, df = 6.34, p = .21). The average number of patients

with medication changes in the TCG was (M = 1.33, SD = 1.03). The mean number of

medication changes in the UCG was (M = .70, SD = .48). See Table 9 for a summary of

the statistical tests for all dependent variables.

Interventions by Nurses and PCPs

During the 30-day periods for all of the patients, there were a total of 36 patient

visits by home healthcare nurses: one patient had three visits, one had four visits, three

had seven visits, one had eight visits, and ten had no visits. There were 16 patient visits

to PCPs: eight patients had one visit, four had two, and four had none. All of the

healthcare provider visits were routine visits. None were the result of a CardioNet alert.

The research hypothesis is that the availability of the CardioNet data would result

in interventions in the TCG that were significantly different than in the UCG. Although

none of the nursing or PCP visits were the result of a CardioNet alert, there were

differences in the number of the routine visits by patients. An independent samples t-test

was conducted to examine whether there was a significant difference between the

numbers of visits in the TCG versus the UCG. The tests revealed that there was no

statistically significant difference between the numbers of nursing or PCP visits between

groups. See Table 9 for a summary of the statistical tests for all dependent variables.

Interventions by Cardiologists

During the 30-day periods for all of the patients, there were 22 visits to

cardiologists: nine patients had one visit, five had two, one had three, and one had none.

100

The six patients in the TCG had a total of 11 cardiologist visits. The 10 patients in the

UCG also had a total of 11 cardiologist visits. All of the cardiologist visits were routine

visits. None were the result of a CardioNet alert.

The research hypothesis is that the availability of the CardioNet data would result

in interventions in the TCG that were significantly different than in the UCG. Although

none of the cardiologist visits were the result of a CardioNet alert, there was a significant

difference in the number of routine visits by patients. An independent samples t-test was

conducted to examine whether the difference between the numbers of visits in the TCG

versus the UCG was significant. The test revealed that there was a statistically

significant difference between the numbers of cardiologist visits between groups (t =

2.22, df = 14, p = .04). The average number of patients with cardiologist visits in the

TCG was (M = 1.83, SD = .75). The mean number of cardiologist visits in the UCG was

(M = 1.10, SD = .57). See Table 9 for a summary of the statistical tests for all dependent

variables.

Although the use of CardioNet in the TCG did not directly result in any

interventions triggered by an alert, the number of visits to cardiologists in the TCG was

66% higher than for the UCG. The patients in the TCG had a significantly lower EF (t =

-2.26, df = 14, p = .04). The mean EF in the TCG was (M = .27, SD = .14). The mean

ejection fraction of patients in the UCG was (M = .47, SD = .18). The t-test confirmed

that the patients in the TCG had weaker hearts than patients in the UCG. Although the

difference in the number of visits was statistically significant, there were no visits that

were triggered by a CardioNet alert.

101

Round-trip visits to the ED

The CardioNet monitoring service included the ability of the service to activate a

call to the EMS if the data indicated a medical emergency or if the patient pressed a

button on the CardioNet monitor. During the 30-day periods for all of the patients, there

were four round-trip visits to the ED. Two patients in the UCG and one in the TCG were

self-referred to the ED. The patient in the TCG who had self-referred to the ED

subsequently presented him or herself at the ED for a second time after being referred by

a physician. None of the four round-trip ED visits was the result of a CardioNet alert.

The research hypothesis is that the availability of the CardioNet data would result

in a significantly different number of ED visits in the TCG versus the UCG. Although

none of the ED visits were the result of a CardioNet alert, there were differences in the

number of ED visits by patients. An independent samples t-test was conducted to

examine whether there was a significant difference between the numbers of visits in the

TCG versus the UCG. The tests revealed that there was no statistically significant

difference between the numbers of visits between groups. See Table 9 for a summary of

the statistical tests for all dependent variables.

102

Table 9

Summary Of The Statistical Tests For All Dependent Variables

Variable Group N Mean Std.

Deviation

t df p

Readmissions TCG 6 .00 .00

-.76 14 .46 UCG 10 .10 .32

Medication

Changes

TCG 6 1.33 1.03 1.41 6.34 .21

UCG 10 .70 .48

Nurse Visits TCG 6 .67 1.63

-1.91 13.38 .08 UCG 10 3.2 3.61

PCP Visits TCG 6 1.17 .98

.61 7.04 .56 UCG 10 .90 .57

Cardiologist Visits TCG 6 1.83 .75

2.22 14 .04 UCG 10 1.10 .57

ED Visits TCG 6 .33 .82

.44 14 .67 UCG 10 .20 .42

Hypothesis Testing

Ho1: The null hypothesis is that there would be no difference in CHF patient

readmissions to the hospital between the TCG and the UCG. Since there was only one

readmission and it was not related to CardioNet telemonitoring, the independent variable,

the null hypothesis is not rejected.

H02: The null hypothesis is that there would be no significant difference in the

number or type of interventions between the TCG and the UCG. The secondary variables

included the number of medication changes, the number of interventions by a nurse,

primary care physician, cardiologist or other specialist, or a round-trip to the ED. Since

none of the secondary endpoints had a relationship with the independent variable, this

null hypothesis is also not rejected.

103

Summary

A population sample (N = 344) was assessed for inclusion in the cardiac

telemonitoring study. Two hundred and eighty-eight patients were excluded, 30 declined

to participate, and 10 withdrew after having been included in the study. The result was a

secondary data archive for the OR study of 16 patients who completed the study. The

study groups, TCG and UCG, included patients with very similar baseline characteristics.

Both groups received routine care from nurses, PCPs, and cardiologists, but there were no

interventions caused by alerts from CardioNet telemonitoring. There was one instance of

the primary endpoint of hospital readmission, but the admission was based on a physician

referral, not a telemonitoring alert.

104

Chapter 5

Implications, Recommendations, and Conclusion

Chapter 4 presented a description of the archive of secondary data used for the

OR study and the results of the study based on the design described in chapter 3. The

purpose of chapter 5 is to discuss implications, recommendations, and a conclusion. The

chapter includes a discussion of the study results, the relationship of the study to other

studies, assumptions and limitations, implications, recommendations for healthcare

leadership, and proposed future research.

Study Results

The purpose of the study was to determine if the use of telemonitoring in the

home, which provides alerts to cardiologists, could result in a reduction in the number of

hospital readmissions within 30 days of a CHF patient’s discharge from the hospital. A

second purpose was to determine if the telemonitoring would have an effect on the

number and type of interventions. Due to the high exclusion rate, the population sample

size was small and lacked statistical power. There was only one hospital readmission and

it was not a result of the independent variable. Further, there were no provider or

medication interventions that arose because of the independent variable.

The OR study was narrowly focused on the patient and whether his or her

readmissions to the hospital could be reduced through CardioNet alerts leading to

interventions. The small sample size limited the number of alerts and interventions that

were measured. In addition to the alerts, CardioNet telemonitoring provided a significant

amount of clinical data to the cardiologists. The CardioNet clinical data and reports,

discussed in the literature review in chapter 2, were beyond the scope of the OR study,

105

but it is possible that the data collected will be useful to the cardiologists in their on-

going care of the patients. There was a statistically significant difference in the number

of routine cardiologist visits between the two groups.

There are two possible explanations for the difference in the number of

cardiologist visits between the two groups. The first reason arises from the effect of the

EF as a confounding variable. The patients in the TCG had weaker hearts than patients in

the UCG and may have caused the cardiologists to follow them more closely. The other

possible explanation for the significantly larger number of cardiologist visits from

patients in the TCG is that the cardiologist had substantial data from CardioNet, including

daily reports with detailed data about the activity of the TCG patient’s heart activity. The

availability of the extra data, not available for patients in the UCG, may have caused the

cardiologists to want to follow the patients more closely to corroborate the data against

their in-person evaluation of the patients. The secondary dependent variable related to

cardiologist visits was visits resulting from a CardioNet alert, not a routine visit.

Although the difference in the number of routine visits was statistically significant, there

were no visits that were triggered by a CardioNet alert.

The primary dependent variable was the number of 30-day all-cause hospital

readmissions. During the 30-day periods for all of the patients, only one hospital

readmission occurred. There was one hospital admission during the course of the study.

A patient in the UCG was admitted to the cardiac department after having a medical

event and being referred by a physician. The research hypothesis is that there would be a

statistically significant difference between the number of hospital readmissions for the

106

TCG and UCG, but the statistical analysis was not able to confirm a significant

difference.

Physicians prescribe changes to the medications being taken by a patient based on

symptoms, laboratory tests, or data from cardiac monitoring. During the 30-day periods

for all of the patients, physicians ordered medication changes to patients in both groups.

The research hypothesis is that the availability of the CardioNet data would result in

medication interventions in the TCG that were significantly different than in the UCG.

However, the results did not confirm that there was any statistically significant difference

between the numbers of medication changes between groups.

Approximately half of the patients in the study received one or more visits from

home healthcare nurses, and most of the patients visited a PCP or cardiologist. The

research hypothesis is that the availability of the CardioNet data would result in provider

interventions in the TCG that were significantly different than in the UCG. Although

none of the provider visits were the result of a CardioNet alert, there were differences in

the number of routine visits, and in the case of cardiologist visits, the difference was

statistically significant.

None of the round-trip ED visits was the result of the CardioNet monitoring. The

research hypothesis is that the availability of the CardioNet data would result in a

significantly different number of ED visits in the TCG versus the UCG. None of the ED

visits were the result of a CardioNet alert and there was no significant in the number of

ED visits.

Ho1: The null hypothesis is that there would be no difference in CHF patient

readmissions to the hospital between the TCG and the UCG. Since there was only one

107

readmission and it was not related to CardioNet telemonitoring, the independent variable,

the null hypothesis is not rejected.

H02: The null hypothesis is that there would be no significant difference in the

number or type of interventions between the TCG and the UCG. The secondary variables

included the number of medication changes, the number of interventions by a nurse,

primary care physician, cardiologist or other specialist, or a round-trip to the ED. Since

none of the secondary endpoints had a relationship with the independent variable, this

null hypothesis was not rejected.

Discussion

Of the 344 patients with a primary diagnosis of CHF who were discharged from

the hospital during the study period, 288 were excluded from the study for various

reasons described in chapter 4. The large number of exclusions highlighted the degree of

chronic illness among the largest category of hospital admissions. If the patients who

were excluded were at least as ill as the patients in the study, they had multiple

comorbidities and were taking multiple heart medications. In the past, the focus has been

on a patient-by-patient basis. Each time a CHF patient was admitted to the hospital, they

were treated and discharged. There was no follow-up across the continuum of care in the

community. The recent shift to patient-centered care is causing hospital administrators

and clinicians to view CHF patients as a population with shared needs for preventive and

post-discharge care.

Approaching CHF patients as a population opens new opportunities for care.

Although there is no cure for CHF, the shift from fee-for-service-based model of care to

an accountability-oriented fee-for-value-based model of care may lead to new healthcare

108

programs for the community of CHF patients. For example, community clinics could

provide instruction in best practices for diet, exercise, identification of symptoms,

importance of medication adherence, and actions to take when an episodic event occurs.

Patient-centered medical homes could proactively reach out to CHF patients to offer

assistance, provide medication reconciliation, and schedule periodic doctor visits.

Physicians or hospitals could offer seminars to recommend mHealth applications and

provide tutorials on how to use them and how to share the data that the apps capture. The

various educational and preventive programs would be prohibitive from a human

resource and financial perspective, but applied at a community level, they may be cost-

effective.

The community hospital study, which produced the secondary anonymized

archive of data, was narrowly focused on data collected from the CardioNet monitor.

The goal was to determine if the data could predict impending heart failure and provide

an opportunity for cardiologists to intervene and prevent hospital readmissions. Since a

comparable study had not previously been performed, there was no disconfirming

evidence, counter-examples, or viable alternative interpretations to consider.

The independent variable in the study was the application of the CardioNet home

telemonitoring. The primary dependent variable was a readmission to the hospital. The

secondary variables included the number and type of interventions, including medication

changes, hospital admissions, and the visits from primary care physicians, cardiologists,

or nurses. The study period for each participant was 30 days. Upon discharge from the

hospital, participants who had consented to the study were given CardioNet equipment

and instructed on how to use it. During each week of the study, a research associate

109

gathered data for each dependent variable. This was accomplished through queries to the

electronic medical records and by telephone interviews. For each of the data, the

measurement was discrete: generally a value of one or none (more than one was possible

but unusual). At the end of the 30 days, the telemonitoring equipment was returned to the

vendor, CardioNet, and, therefore, drawing new samples from the original data or other

bootstrapping techniques were not possible.

Relationship to Other Studies

Most studies found in the literature used multiple medical devices such as blood

pressure cuffs, oxygenation sensors, and weight scales to gather data from the patient.

Such devices are often supplemented with interactive devices or telephone call center

interaction to gather additional information from patients. Studies of this nature require

significant involvement of the patient and, as noted in the Chaudhry et al. (2010) study,

can result in reduced compliance. The CTH study, which was the source of the data for

the OR study, offered the potential for a minimally intrusive and easy-to-use approach to

gathering data from the patient.

The CardioNet telemonitoring approach was unique in that it gathered data

directly from the heart of the patient. Singh et al. (2012) said that blood pressure and

weight are related to impending heart failure, but the warning comes too late. He

suggested that the only way to gain accurate and timely information about an impending

heart problem was to directly measure the activity of the heart. That was a supporting

reason for the CardioNet approach – to follow the theory that Singh et al. (2012)

advocated.

110

The purpose of the OR study was to determine if the application of CardioNet

telemonitoring could provide a warning sign to cardiologists that could result in reduced

readmissions or changes in care management. The data analysis was not able to reject the

null hypothesis that no such relationships exist. With regard to disconfirming evidence,

counter-examples, or viable alternative interpretations, the literature provides a wide

range of outcomes from the use of telemonitoring.

The Chaudhry et al. (2010) study showed no relationship between traditional

telemonitoring of weight, oxygenation, and blood pressure and readmissions. However,

other studies that combined telemonitoring with expanded home health care, hospital

outreach, and other enhancements across the continuum of care showed statistically

significant reduced readmissions. No studies were found that focused narrowly on the

use of non-invasive telemonitoring of heart activity such as provided by the CardioNet

technology. The recent expansion in the number of consumer mHealth devices for home

telemonitoring opens a significant opportunity for new research designs.

Strengths and Weaknesses

There are many CHF telemonitoring studies that used traditional weight, blood

pressure, oxygenation, and a question dialog via telephonic voice response systems.

Using CardioNet Mobile Cardiac Outpatient Telemetry™ technology for telemonitoring

of CHF patients was a first of a kind study. The CardioNet technology was typically

used to detect arrhythmia and not been used to predict heart failure in CHF patients. The

literature suggested that implantable monitoring may be the best approach to predict

impending heart failure, but the cost and complexity of such a study for CTH was not

feasible.

111

The CardioNet study offered the potential to use non-invasive mHealth

technology with minimal patient participation in the monitoring. The theory of being

able to predict impending heart failure based on data about the activity of the heart was

unprecedented, but of interest to the chief of cardiology at the community hospital. The

CardioNet study was reviewed and approved by the chief medical officer, the chief

nursing officer, the director of graduate medical education and research, the director of

clinical research, and the chief of emergency medicine, and the senior vice president of

clinical operations at CardioNet.

A second strength is that the OR study places the importance of community

epidemiology in clear focus. The provider entitlement-oriented fee-for-service-based

model is in transition to an accountability-oriented fee-for-value-based model. The

acuity of the illness of the eligible patients for the study highlights the need for a broader

approach to healthcare for CHF patients. The study recommendations can inform

healthcare administrators and clinicians of the importance of a community healthcare

approach.

The most significant weakness of the study was the small sample size that

resulted from the unexpected large exclusion rate. In retrospect, a better recruitment plan

could have resulted in a larger sample. A large portion of the exclusions were due to

scheduling difficulties, where patients for whom the research associate identified the

patient as eligible for the study, but the protocol-required approval of a cardiologist was

not able to be obtained before the patient was discharged.

Although the study highlighted some important community-wide issues, the small

sample size did not provide sufficient statistical power to make any judgments about the

112

relationship between the independent variable, use of CardioNet home telemonitoring,

and hospital readmissions or the number or type of interventions.

Another unanticipated negative influence on the number of patients able to

complete the study was the CardioNet technology itself. Some patients became allergic

to the adhesive of the sensors that were attached to their skin. CardioNet was responsive

to this issue and provided hypoallergenic sensors, but it was not possible to gain a

sufficient number of patients to offset those that had decided to exit the study. A further

unanticipated factor was that some patients were overwhelmed with the technology and

chose to exit the study.

Technology for Monitoring

The CardioNet technology consisted of a three-lead pendant that was worn around

the patient’s neck. Three leads from the pendant were attached to the patient using an

adhesive on the back of the sensors. The sensors sampled the activity of the heart 250

times per second. A handheld monitor, resembling a smartphone, collected the data and

provided a way for the patient to press a button if they had an incident or uncertain

feeling.

An algorithm running in the monitor looked for abnormalities in the rhythm of the

heart and data were transferred to a CardioNet monitoring service via the Internet for

further analysis. The technology would seem trivial to a teenager, but to an ill 75-year

old patient, it could prove to be overwhelming. Four of the 10 patients who withdrew

from the CTH study did so for reasons related to the technology – three were

overwhelmed with the monitor and one had an allergic reaction to the adhesive.

113

CardioNet subsequently provided hypoallergenic sensors to reduce the possibility of

adhesive reactions.

Singh et al. (2012) said that the best way to capture data about the heart activity of

a patient is by using an implantable device. An implantable technology may provide the

most accurate data and require no patient involvement. However, a surgical procedure is

required to insert the implantable device. Considering an implantable device for a

significant research study could present cost and recruitment challenges. There have

been studies comparing the effectiveness of various implantable devices but there is scant

data supporting clinical efficacy (Singh et al., 2012). One major trial (CHAMPION)

demonstrated that the implantable CardioMEMS Heart Failure Sensor could significantly

reduce heart failure hospitalizations (Singh et al., 2012).

The technological landscape in healthcare devices is changing rapidly. The IMS

Institute for Healthcare Informatics issued a report that includes an analysis of more than

40,000 healthcare apps available from Apple’s iTunes store (IMS, 2013). The study

found that more than half of the apps were not relevant to patient health, but that many

are. For example, AliveCor has a heart monitor that works as an attachment to the Apple

iPhone. The device is an FDA-approved, single-channel ECG recorder that produces a

30-second ECG that can be stored, displayed, and shared with a doctor (Alivecor, 2013).

The AliveCor device was not available at the time the CTH study was designed, but such

a device may be an alternative for future studies. The ease of use and lack of sensors

attached to the skin might improve recruitment and compliance.

In addition to the many smartphone apps and related devices, major companies

such as GE are investing in new healthcare device technologies. For example, GE has

114

developed a pocket mobile echocardiography (PME) device called the Vscan that could

potentially replace the stethoscope. The device is the size of a cell phone, portable, with

inherent wireless potential and has wide-ranging possibilities that stretch well beyond

cardiovascular care. Liebo et al. (2011) described how the PME can provide portable,

fast, and non-invasive images of the internal structures of the heart and how the PME has

the potential to replace the standard echocardiogram. (Topol, 2012) went further and said

that devices such as the PME may eventually replace 75% of echocardiograms that are

performed in hospitals or physician offices.

Assumptions and Limitations

Assumptions

The OR study assumed that CTH would be able to recruit 130 patients and create

the archive of secondary data in five months. The hospital developed its research plan on

the basis of 35 months of history of hospital discharges with a primary diagnosis of CHF

from September 2009 through June 2012. The average discharges for that period were

42.4 per month. The assumption about the number of discharges turned out to be correct,

but the assumption that 20% of those discharges would be excluded from the study turned

out to be significantly less than the actual exclusion rate of 84%. There was no

comparable history at the hospital or in the literature to suggest that the 20% exclusion

assumption would not be attainable.

Limitations

A significant limitation resulted from the unanticipated impact of the Medicare

shift in reimbursement policy. Section 3025 of the Patient Protection and Affordable

Care Act (ACA) added a new section to the Social Security Act establishing the Hospital

115

Readmissions Reduction Program (HRRP) (CMS, 2013). The HRRP requires CMS to

reduce payments to hospitals with excessive readmissions, effective for discharges

beginning on October 1, 2012. Because the reduced payments would be based on

performance relative to other hospitals, the amount of impact to the CTH was not known

until August of 2013 (Rau, 2013). The combination of potential new fines and a large

reduction in the financial support to community hospitals from the state government

resulted in a sharp focus on hospital readmissions by the CTH CEO. The focus resulted

in the hospital leadership team developing a strategy to engage participants in the

continuum of care across the community that the network hospitals serve.

The hospital strategy included implementation of new programs that the

leadership believes can result in reduced unnecessary hospital admissions. These

programs included new patient-centered medical homes (PCMHs) and nurse navigators

following up with CHF patients upon discharge to ensure PCP appointments and timely

medication reconciliation. In addition, cardiologists have been aggressively following

and treating their CHF patients. The visiting nurses association (VNA) and the hospital’s

home health services department have been offering more services for CHF patients,

including telemonitoring. Finally, community support for hospice care has made it a

more acceptable alternative to hospital care.

The resulting trend toward fewer unnecessary admissions for the reasons

discussed resulted in higher acuity for those patients who were admitted. The baseline

characteristics of the 26 subjects who were randomized into the TCG and UCG indicated

that most enrolled patients suffered from significant heart failure. The average ejection

fraction, a measure of the performance of the heart, was .39, compared to .55 for a

116

healthy person (Grogan, 2013). All but one of the patients had at least one significant

comorbidity and were taking at least two heart medications. The exclusion criteria

described in chapter 3, which were mostly related to the health of the patient, resulted in

more than 80% of potential study participants being excluded.

Implications

The first implication of the OR study is that the degree of illness of people with

CHF goes beyond the individual and reaches into the community. CHF is the number

one cause of hospitalization (Dang et al., 2009). In the fee-for-service-based healthcare

model, hospitals were compensated each time they saw a patient or performed any

service for them. CHF patients were treated on a patient-by-patient, incident-by-incident

basis.

The recruitment process that preceded the creation of the data archive for use in

the OR study highlighted that there is a subset of the community that is suffering from a

disease that causes reduced quality of life for patients and families. As healthcare reform

under the ACA continues to gain traction and cause a shift from the entitlement-oriented,

fee-for-service-based model to an accountability-oriented, fee-for-value-based model,

hospitals will be motivated to look at key illnesses such as CHF at a population level. A

thorough understanding of the demographics and medical condition of such a population

subset could enable hospitals to develop community-based preventive care programs and

clinics to address the needs of CHF patients in the community.

A second implication is that a community hospital research study involving CHF

patients requires a community-wide effort extending beyond a single hospital. Including

several community hospitals to increase the number of candidates could offset a low rate

117

of recruitment. Increasing the awareness of telemonitoring among the medical staff and

in the community and articulating the potential benefits of telemonitoring studies could

potentially result in higher enrollment of study participants.

As the ACO model of care causes providers to think about the population for

which they are caring, gaining insight from data about that population will become more

important. For example, cardiologists may see data from a CHF research study as a way

to develop care plans that are more effective than plans developed at an individual level.

For example, detailed data from CardioNet or other monitoring technologies could enable

cardiologists to make changes in a drug, drug dosage, or frequency of taking a drug, and

then see the impact on the activity of the heart of the patient. Likewise, home healthcare

services may be able to use data from a CHF study to help them refine their care delivery

programs. PCPs participating in patient-centered medical homes (PCMHs) are beginning

to see the benefits of gaining more data about CHF patients (Nutting et al., 2010). PCPs,

cardiologists, and home healthcare services collectively could encourage the patients they

see on a regular basis to participate in research studies resulting in larger scale studies

that could provide the statistical power to reject or fail to reject various hypotheses.

Proposed Future Research

There are two areas of focus recommended for consideration as future research.

First is to use a similar protocol for measurement of variables to what CTH used, but with

different technology approach. As discussed in the Technology for Monitoring section of

this chapter, consumer medical devices and apps have proliferated, and using a

smartphone app may be more acceptable and easier to use for patients. An around-the-

118

clock telemonitoring service with sensors attached to the body may not be needed to

gather predictive data about a patient’s condition.

An investigation of technology options could result in finding a new approach to

telemonitoring that is affordable and easy to use. For example, Lau et al. (2013) enrolled

109 patients in a study where an iPhone ECG was taken within six hours of a traditional

12-lead ECG. The researchers found that the iPhone with the AliveCor attachment could

produce a single-lead ECG that provided similar results to the traditional 12-lead ECG.

The researchers suggested that the iPhone/AliveCor combination could make mass ECG

screenings cost-effective.

In the event that the technology options suggested might result in small sample

sizes, a re-sampling method, such as bootstrapping, should be considered to supplement

the traditional statistical analysis. I performed a bootstrapping analysis using SPSS with

1,000 samples from the OR study, and the results showed no statistically significant

relationship between the use of the 30-day CardioNet telemonitoring and any of the

dependent variables. However, in the event that the hospital conducts a community-wide

follow-on study using consumer mHealth technologies, more dependent variable

measurements could be taken and provide the opportunity for re-sampling.

A second area recommended for study is within the big data that exists about CHF

patients. Brown, Chui, and Manyika (2011) defined big data as “Large pools of data that

can be captured, communicated, aggregated, stored, and analyzed.” Big data is an

important part of every sector of the global economy, and healthcare is no exception. For

each of the 344 patients that were assessed for inclusion in the CTH study, there is an

EMR containing data about the patient.

119

For every ED and hospital admission, the hospital captures demographic and

other baseline data, detailed records of medications, tests, and procedures. With financial

incentives from CMS, the number of community physicians using EMRs has increased

dramatically (Swanson, Cowan, & Blake, 2011), and the development of health

information exchanges (HIEs) will enable all the providers in the community to share

data about patients, within HIPAA regulations (Menachemi, Matthews, Ford, Hikmet, &

Brooks, 2009). The development of the MBAN will allow cardiac monitoring at home

and enable real-time data about the heart to flow directly from the MBAN, over the

Internet, into an EMR. The combination of these factors will result in a vast amount of

data. All of this data, plus other epidemiologic data that may be available from Federal,

state and local sources, represents a big-data repository that could be studied for insight

about the community of CHF patients.

The combination of big-data and analytics can enable researchers and healthcare

leaders to ask previously difficult to answer questions. For example, a cardiologist might

ask what percentage of all CHF patients in the community, served by his or her practice,

who are males over the age of 65 and have been readmitted at least once in the past 12

months are currently taking a beta-blocker. A healthcare planning analyst might develop

a model to predict the number of CHF readmissions to expect in the coming year. A

descriptive statistics study of the CHF population could serve as the basis to design new

studies and formulate hypotheses to answer key research questions that are relevant to the

hospital’s mission to care for the population of the community it serves. Such studies,

combined with epidemiologic data, could inform healthcare leaders to develop programs

120

that replace patient-by-patient treatment with preventive medical programs and clinics

that address health problems on a community basis.

Recommendations for Healthcare Leadership

While identifying the optimum technology and techniques for telemonitoring that

can reliably predict impending heart failure and provide interventions to reduce

readmissions has been challenging, recent studies suggest it can be cost effective

(Thokala et al., 2013). Continued telemonitoring research may be a sound investment,

and technology and breadth of study should be considered. Effective studies should

leverage the advances being made in technology and incorporate support across the

community.

Healthcare leadership should evaluate mobile health (mHealth) technologies.

Public health and medical practice are becoming widely supported by mHealth devices

spanning a variety of application areas including the use of smartphones to improve care

delivery, patient communications, point of service data collection, and the use of

alternative wireless devices for adherence support, telemonitoring, and real-time

medication monitoring (Tomlinson, Rotheram-Borus, Swartz, & Tsai, 2013). The newer

technologies have the potential to remove the shortcomings of past technologies, as

discussed in the literature review in chapter 2.

A robust partnership should be developed with community providers to ensure

recruitment of a sufficient number of participants. The CTH chief of cardiology and

director of research did an excellent job of communicating with cardiology practices by

briefing them on the study process and potential benefits. In future studies, the

partnership could be expanded to include the home health services organization and the

121

VNA, hospitalists, and skilled nursing facilities. A broad-based partnership could

increase awareness and boost recruitment efforts, resulting in more statistical power and

generalizability.

The shift to the ACO model will require that providers understand as much as

possible about the population it cares for. Kayyali, Knott, and Kuiken (2013) wrote that

big-data applications could provide transparency to the health of a community and drive

improved patient outcomes, reduce readmissions, and eventually reduce American

healthcare cost by $300 billion. Healthcare leaders need to ensure they have robust plans

for exploiting their data.

Healthcare leadership should evaluate the role of epidemiology in the

organization’s strategic planning. An epidemiologic approach would use the science of

public health and prevention as a tool to examine the etiology of disease on a population

basis. The data exists. The next step is to build an analytics platform to extract

information and understanding from the data for the benefit of the community.

Conclusion

A population sample (N = 344) was assessed for inclusion in the cardiac

telemonitoring study. Two hundred and eighty-eight patients were excluded, 30 declined

to participate, and 10 withdrew after having been included in the study. The result was a

secondary data archive for the OR study of 16 patients who completed the study. The

study groups, TCG and UCG, included patients with very similar baseline characteristics.

Both groups received routine care from nurses, PCPs, and cardiologists, but there were no

interventions caused by alerts from CardioNet telemonitoring.

122

There were differences in the secondary dependent variables, with only the

number of routine cardiologist visits proving to be statistically significant. There was one

instance of the primary endpoint of hospital readmission, but the admission was based on

a physician referral, not a telemonitoring alert.

Interest in home telemonitoring for the reduction of hospital readmissions for

CHF patients is growing, but a consistent and positive impact on readmissions remains

elusive (Smith, 2013). However, the projected increase in the number of older adults and

high incidence of CHF increase the need to find a reliable and cost-effective means to

provide alerts for interventions that can improve health outcomes and improve quality of

life. New mHealth technologies and a broad partnership across the community and the

continuum of care can make more effective care programs and larger studies possible.

Telemonitoring may be able to play a significant role as technology improves to

make implementation simpler for providers and compliance easier for patients. Heart

failure is the leading cause of mortality in the world’s population and the OR study has

highlighted the significance of the disease among the population served by the

community teaching hospital. Treatment of individual patients is important but the larger

opportunity is to gain an understanding of the epidemiologic factors affecting the

community population. The resulting insight could provide a basis to develop new

standards of care and lead to improved patient safety and a higher quality of care, as well

as improved quality of life for patients and their families.

123

References

The American Association of Cardiovascular and Pulmonary Rehabilitation (2012).

Promoting health & preventing disease. Retrieved from

https://http://www.aacvpr.org/Default.aspx

Abramson Center for the Future of Health. (2012). The blue box project: A new paradigm

for managing chronic disease. Retrieved from

http://www.theabramsoncenter.org/en/programs/blue-box/blue-box-in-depth

AliveCor. (2013). Ecg screening made easy. Retrieved from http://www.alivecor.com/en

Allaudeen, N., Schnipper, J., Orav, E., Wachter, R., & Vidyarthi, A. (2011). Inability of

providers to predict unplanned readmissions. JGIM: Journal of General Internal

Medicine, 26(7), 771-776. doi:10.1007/s11606-011-1663-3

American Heart Association. (2012). About arrhythmia. Retrieved from

http://www.heart.org/HEARTORG/Conditions/Arrhythmia/AboutArrhythmia/Ab

out-Arrhythmia_UCM_002010_Article.jsp

Andrietta, M., Lopes-Moreira, R., & Bottura Leite de Barros, A. L. (2011). Hospital

discharge plan for patients with congestive heart failure. Revista Latino-

Americana de Enfermagem, 19(6), 1445-1452. Retrieved from

http://www.scielo.br/scielo.php?pid=0104-1169&script=sci_serial

Antonicelli, R., Mazzanti, I., Abbatecola, A. M., & Parati, G. (2010). Impact of home

patient telemonitoring on use of beta-blockers in congestive heart failure. Drugs

and Aging, 27(10), 801-805. doi:10.2165/11538210-000000000-00000

Antonicelli, R., Testarmata, P., Spazzafumo, L., Gagliardi, C., Bilo, G., Valentini, M., . . .

Parati, G. (2008). Impact of telemonitoring at home on the management of elderly

124

patients with congestive heart failure. Journal of Telemedicine and Telecare,

14(6), 300-305. doi:10.1258/jtt.2008.071213

Asch, D. A., Muller, R. W., & Volpp, K. G. (2012). Automated hovering in health care

— watching over the 5000 hours. The New England Journal Of Medicine, 367(1),

1-3. doi:DOI: 10.1056/NEJMp1203869

Aspenson, M., & Hazary, S. (2012). The clock is ticking on readmission penalties.

Healthcare Financial Management: Journal Of The Healthcare Financial

Management Association, 66(7), 58-63. Retrieved from http://www.hfma.org/

Augustin, U., & Henschke, C. (2012). Does telemonitoring lead to health and economic

benefits in patients with chronic heart failure?: A systematic review.

Gesundheitswesen. doi:10.1055/s-0032-1309021

Austin, A., & Wetle, V. L. (2012). The United States health care system: Combining

business, health, and delivery. Upper Saddle River, N.J.: Pearson.

Averill, R. F., McCullough, E. C., Hughes, J. S., Goldfield, N. I., Vertrees, J. C., &

Fuller, R. L. (2009). Redesigning the medicare inpatient pps to reduce payments

to hospitals with high readmission rates. Health Care Financing Review, 30(4), 1-

15. Retrieved from https://http://www.cms.gov/HealthCareFinancingReview/

Bakhshi, S., Li, X., Semenov, N., Apodaca-Madrid, J., Mahoor, M. H., Newman, K. E., .

. . Neuman, C. (2011). Congestive heart failure home monitoring pilot study in

urban denver. Conf Proc IEEE Eng Med Biol Soc, 2011, 3150-3153.

doi:10.1109/iembs.2011.6090859

125

Bates, D. W., & Bitton, A. (2010). The future of health information technology in the

patient-centered medical home. Health Affairs, 29(4), 614-621.

doi:10.1377/hlthaff.2010.0007

Bausell, R. B., & Li, Y.-F. (2002). Power analysis for experimental research : A

practical guide for the biological, medical and social sciences. West Nyack, NY:

Cambridge University Press.

Berenson, R. A., Paulus, R. A., & Kalman, N. S. (2012). Medicare's readmissions-

reduction program — a positive alternative. New England Journal of Medicine,

366(15), 1364-1366. doi:10.1056/NEJMp1201268

Berkman, B., & Abrams, R. D. (1986). Factors related to hospital readmission of elderly

cardiac patients. Social Work, 31(2), 99. Retrieved from

http://www.naswpress.org/publications/journals/sw.html

Bird, S., Noronha, M., & Sinnott, H. (2010). An integrated care facilitation model

improves quality of life and reduces use of hospital resources by patients with

chronic obstructive pulmonary disease and chronic heart failure. Australian

Journal of Primary Health, 16(4), 326-333. Retrieved from

http://www.publish.csiro.au/nid/261.htm

Blanton, S., Morris, D. M., Prettyman, M. G., McCulloch, K., Redmond, S., Light, K. E.,

& Wolf, S. L. (2006). Lessons learned in participant recruitment and retention:

The excite trial. Physical Therapy, 86(11), 1520-1533. doi:10.2522/ptj.20060091

Blasco, A., Carmona, M., Fernandez-Lozano, I., Salvador, C. H., Pascual, M., Sagredo,

P. G., . . . Alonso-Pulpon, L. (2012). Evaluation of a telemedicine service for the

126

secondary prevention of coronary artery disease. Journal of Cardiopulmonary

Rehabilitation Prevention, 32(1), 25-31. doi:10.1097/HCR.0b013e3182343aa7

Bohm, M., Swedberg, K., Komajda, M., Borer, J. S., Ford, I., Dubost-Brama, A., . . .

Tavazzi, L. (2010). Heart rate as a risk factor in chronic heart failure (shift): The

association between heart rate and outcomes in a randomised placebo-controlled

trial. Lancet, 376(9744), 886-894. doi:10.1016/s0140-6736(10)61259-7

Bowles, K. H., Holland, D. E., & Horowitz, D. A. (2009). A comparison of in-person

home care, home care with telephone contact and home care with telemonitoring

for disease management. Journal of Telemedicine and Telecare, 15(7), 344-350.

doi:10.1258/jtt.2009.090118

Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of 'big data'?

(cover story). McKinsey Quarterly(4), 24-35.

Burns, J., Serber, E. R., Keim, S., & Sears, S. F. (2005). Measuring patient acceptance of

implantable cardiac device therapy: Initial psychometric investigation of the

Florida patient acceptance survey. Journal of Cardiovascular Electrophysiology,

16(4), 384-390. doi:10.1046/j.1540-8167.2005.40134.x

CardioNet. (2012). CardioNet MCOT™. Retrieved from

https://http://www.cardionet.com

Cawley, J., & Grantham, C. C. (2011). Building a system of care: Integration across the

heart failure care continuum. Permanente Journal, 15(3), 37-42. Retrieved from

http://www.thepermanentejournal.org/

Centers for Medicare & Medicaid Services. (2012a). HCAHPS background. Retrieved

from http://www.hcahpsonline.org/home.aspx

127

Centers for Medicare & Medicaid Services. (2012b). Hospital consumer assessment of

healthcare providers and systems. Retrieved from

http://www.hcahpsonline.org/executive_insight/default.aspx

Centers for Medicare & Medicaid Services. (2012c). Telemedicine. Retrieved from

http://cms.hhs.gov/Telehealth/

Chaudhry, S. I. (2007). Randomized trial of telemonitoring to improve heart failure

outcomes (tele-hf): Study design. Journal of Cardiac Failure, 13(9), 709-714.

Retrieved from http://www.hfsa.org/journal.asp

Chaudhry, S. I., Mattera, J. A., Curtis, J. P., Spertus, J. A., Herrin, J., Lin, Z., . . .

Krumholz, H. M. (2010). Telemonitoring in patients with heart failure. The New

England Journal Of Medicine, 363(24), 2301-2309. Retrieved from

http://www.nejm.org/

Cherofsky, N., Onua, E., Sawo, D., Slavin, E., & Levin, R. (2011). Telehealth in adult

patients with congestive heart failure in long term home health care: A systematic

review. JBI Library of Systematic Reviews, 9(30), 1271-1296. Retrieved from

http://www.joannabriggslibrary.org/

Christensen, L. B., Johnson, B., & Turner, L. (2011). Research methods, design, and

analysis. Boston, MA: Allyn & Bacon/Pearson.

Clarke, M., Shah, A., & Sharma, U. (2011). Systematic review of studies on

telemonitoring of patients with congestive heart failure: A meta-analysis. Journal

of Telemedicine and Telecare, 17(1), 7-14. doi:10.1258/jtt.2010.100113

128

Cliff, B. (2012). Excellence in patient satisfaction within a patient-centered culture.

Journal of Healthcare Management, 57(3), 157-159. Retrieved from

http://www.ache.org/pubs/jhmsub.cfm

Centers for Medicare & Medicaid Services. (2013). Readmissions reduction program.

Retrieved from http://www.cms.gov/Medicare/Medicare-Fee-for-Service-

Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html

Congestive heart failure. (2008). In The Columbia Encyclopedia. Columbia University

Press. Retrieved from http://www.encyclopedia.com/

Consumer Electronics Show. (2012). International consumer electronics show. Exhibitor

Directory. Retrieved from http://www.cesweb.org/

Corventis. (2012). AVIVO mobile patient management system. Retrieved from

http://corventis.com/us/avivo.asp

Cowie, M. R., & Davidson, L. (2012). Clinical perspective: The importance of heart rate

reduction in heart failure. International Journal of Clinical Practice, 66(8), 728-

730. doi:10.1111/j.1742-1241.2012.02968.x

Cusack, C. M., Pan, E., Julie, M., Vincent, A., Kaelber, D. C., & Middleton, B. (2008).

The value proposition in the widespread use of telehealth. Journal of

Telemedicine and Telecare, 14(4), 167-168. doi:10.1258/jtt.2007.007043

Dang, S., Dimmick, S., & Kelkar, G. (2009). Evaluating the evidence base for the use of

home telehealth remote monitoring in elderly with heart failure. Telemed J E

Health, 15(8), 783-796. doi:10.1089/tmj.2009.0028

Dar, O., Riley, J., Chapman, C., Dubrey, S. W., Morris, S., Rosen, S. D., . . . Cowie, M.

R. (2009). A randomized trial of home telemonitoring in a typical elderly heart

129

failure population in north west london: Results of the home-hf study. European

Journal of Heart Failure, 11(3), 319-325. doi:10.1093/eurjhf/hfn050

Dendale, P., De Keulenaer, G., Troisfontaines, P., Weytjens, C., Mullens, W., Elegeert,

I., . . . Hansen, D. (2012). Effect of a telemonitoring-facilitated collaboration

between general practitioner and heart failure clinic on mortality and

rehospitalization rates in severe heart failure: The tema-hf 1 (telemonitoring in the

management of heart failure) study. European Journal of Heart Failure, 14(3),

333-340. doi:10.1093/eurjhf/hfr144

Doherty, R. B. (2010). The certitudes and uncertainties of health care reform. Annals of

Internal Medicine, 152(10), 679-682. Retrieved from http://www.annals.org/

Dunderdalea, K., Thompson, D. R., Milesc, J. N. V., Beerd, S. F., & Furzec, G. (2005).

Quality-of-life measurement in chronic heart failure: Do we take account of the

patient perspective? Eur J Heart Fail, 7(4), 572-582.

doi:10.1016/j.ejheart.2004.06.006

Elwyn, G., Hardisty, A. R., Peirce, S. C., May, C., Evans, R., Robinson, D. K., . . .

Preece, A. D. (2011). Detecting deterioration in patients with chronic disease

using telemonitoring: Navigating the 'trough of disillusionment'. Journal of

Evaluation in Clinical Practice. doi:10.1111/j.1365-2753.2011.01701.x

Epstein, A. M. (2009). Revisiting readmissions--changing the incentives for shared

accountability. The New England Journal Of Medicine, 360(14), 1457-1459.

Retrieved from http://www.nejm.org/

130

Erdfelder, E., Faul, F., Lang, A.-G., & Buchner, A. (2007). G*power 3: A flexible

statistical power analysis program for the social, behavioral, and biomedical

sciences. Behavior Research Methods, 39(2), 175-191. doi:10.3758/BF03193146

Farcaş, A. D., & Năstasă, L. E. (2011). Quality of life in patients with chronic congestive

heart failure. Human & Veterinary Medicine, 3(3), 239-245. Retrieved from

http://www.hvm.bioflux.com.ro

Farzanfar, R., Finkelstein, J., & Friedman, R. H. (2004). Testing the usability of two

automated home-based patient-management systems. Journal of Medical Systems,

28(2). Retrieved from

http://www.springer.com/statistics/life+sciences,+medicine+%26+health/journal/

10916

Fayers, P., & Machin, D. (2010). Randomized clinical trials design, practice and

reporting. Hoboken, NJ, USA: Wiley.

Fitzpatrick, J. J., & Wallace, M. (2006). Encyclopedia of nursing research (Second ed.).

New York, NY: Springer Publishing Company, Inc.

Fong, B., Fong, A. C. M., & Li, C. K. (2011). Telemedicine technologies information

technologies in medicine and telehealth. Chichester, West Sussex, U.K.: John

Wiley & Sons.

Friedman, L., Gyr, H., & Gyr, A. (2010). The changing patient in the digital era: A

typology for guiding innovation in healthcare. International Journal of Innovation

Science, 2(1), 39-46. Retrieved from http://www.multi-science.co.uk/ijis.htm

Gagnon, M. P., Orruno, E., Asua, J., Abdeljelil, A. B., & Emparanza, J. (2012). Using a

modified technology acceptance model to evaluate healthcare professionals'

131

adoption of a new telemonitoring system. Telemed J E Health, 18(1), 54-59.

doi:10.1089/tmj.2011.0066

Goldfield, N. (2010). Strategies to decrease the rate of preventable readmission to

hospital. Canadian Medical Association Journal, 182(6), 538-539. Retrieved from

http://www.cmaj.ca/

Greenwald, H. P. (2010). Health care in the United States: Organization, management,

and policy. San Francisco: Jossey-Bass.

Grogan, M. (2013). Ejection fraction: What does it measure? Mayo Clinic. Retrieved

from http://www.mayoclinic.com

Hansen, L., Young, R. S., Hinami, K., Leung, A., & Williams, M. V. (2011).

Interventions to reduce 30-day rehospitalization: A systematic review. Annals of

Internal Medicine, 155(8), 520-528. doi:10.1059/0003-4819-155-8-201110180-

00008

Hasan, O., Meltzer, D. O., Shaykevich, S. A., Bell, C. M., Kaboli, P. J., Auerbach, A. D.,

. . . Schnipper, J. L. (2010). Hospital readmission in general medicine patients: A

prediction model. Journal of General Internal Medicine, 25(3), 211-219.

doi:10.1007/s11606-009-1196-1

Health Indicators Warehouse. (2008). Congestive heart failure admission rate. Retrieved

from http://healthindicators.gov

HealthReformGPS. (2011). Medicare value-based purchasing programs. Retrieved from

http://healthreformgps.org/resources/medicare-value-based-purchasing-programs/

Heart, in anatomy. (2008). In The Columbia Encyclopedia. Columbia University Press.

Retrieved from http://www.encyclopedia.com/

132

Henriques, M. A., Costa, M. A., & Cabrita, J. (2012). Adherence and medication

management by the elderly. Journal of Clinical Nursing, 21(21/22), 3096-3105.

doi:10.1111/j.1365-2702.2012.04144.x

Hernandez, A. F., Fonarow, G. C., Liang, L., Heidenreich, P. A., Yancy, C., & Peterson,

E. D. (2011). The need for multiple measures of hospital quality: Results from the

get with the guidelines-heart failure registry of the american heart association.

Circulation, 124(6), 712-719. doi:10.1161/CIRCULATIONAHA.111.026088

Hesselink, G., Schoonhoven, L., Barach, P., Spijker, A., Gademan, P., Kalkman, C., . . .

Wollersheim, H. (2012). Improving patient handovers from hospital to primary

care: A systematic review. Annals of Internal Medicine, 157(6), 417-428.

doi:10.7326/0003-4819-157-6-201209180-00006

Hines, P. A., Yu, K., & Randall, M. (2010). Preventing heart failure readmissions: Is your

organization prepared? Nursing Economics, 28(2), 74-85. Retrieved from

http://www.nursingeconomics.net

Horton, K. (2008). Falls in older people: The place of telemonitoring in rehabilitation.

Journal of Rehabilitation Research and Development, 45(8), 1183-1194.

Retrieved from http://www.rehab.research.va.gov/jour/jourindx.html

Hsich, E., & Wilikoff, B. (2013). Diseases & conditions. Cleveland Clinic HealthHub.

Retrieved from

http://my.clevelandclinic.org/heart/disorders/heartfailure/ejectionfraction.aspx

IMS Institute for Healthcare Informattics. (2013). Patient apps for improved healthcare:

From novelty to mainstream. Retrieved from http://www.imshealth.com

133

Jacobson, P., & Jazowski, S. A. (2011). Physicians, the affordable care act, and primary

care: Disruptive change or business as usual? J Gen Intern Med. 2011

Aug;26(8):934-7. Epub 2011 Apr 1, 26(8), 934-937. doi:10.1007/s11606-011-

1695-8

Jain, P. C. (2011). Wireless body area network for medical healthcare. IETE Technical

Review, 28(4), 362-371. doi:10.4103/0256-4602.83556

Jencks, S. F. M. M., Williams, M. V. M., & Coleman, E. A. M. M. (2009).

Rehospitalizations among patients in the medicare fee-for-service program. The

New England Journal of Medicine, 360(14), 1418-1428.

doi:10.1056/NEJMsa0803563

Jenkins, K., & Kirk, M. (2010). Heart failure and chronic kidney disease: An integrated

care approach. Journal of Renal Care, 36, 127-135. doi:10.1111/j.1755-

6686.2010.00158.x

Jeon, Y. H., Kraus, S., Jowsey, T., & Glasgow, N. (2010). The experience of living with

chronic heart failure: A narrative review of qualitative studies. BMC Health

Services Research, 10(1), 77. doi:10.1186/1472-6963-10-77

Joynt, K. E., & Jha, A. K. (2012). Thirty-day readmissions — truth and consequences.

New England Journal of Medicine, 366(15), 1366-1369.

doi:10.1056/NEJMp1201598

Joynt, K. E., Orav, E. J., & Jha, A. K. (2011). The association between hospital volume

and processes, outcomes, and costs of care for congestive heart failure. Annals of

Internal Medicine, 154(2), 94-102. Retrieved from http://www.annals.org/

134

Jweinat, J. J. (2010). Hospital readmissions under the spotlight. Journal Of Healthcare

Management / American College Of Healthcare Executives, 55(4), 252-264.

Retrieved from http://www.ache.org/pubs/jhmsub.cfm

Kang, X., Li, Z., & Nolan, M. T. (2011). Informal caregivers' experiences of caring for

patients with chronic heart failure: Systematic review and metasynthesis of

qualitative studies. Journal of Cardiovascular Nursing, 26(5), 386-394.

doi:10.1097/JCN.0b013e3182076a69

Karg, O., Weber, M., Bubulj, C., Esche, B., Weber, N., Geiseler, J., . . . Schellhorn, H.

(2012). [acceptance of a telemonitoring device in patients with chronic

obstructive pulmonary disease]. Deutsche Medizinische Wochenschrift, 137(12),

574-579. doi:10.1055/s-0031-1299033

Kataoka, H. (2009). Novel monitoring method for the management of heart failure:

Combined measurement of body weight and bioimpedance index of body fat

percentage. Future Cardiology, 5(6), 541-546. doi:10.2217/fca.09.47

Kayyali, B., Knott, D., & Kuiken, S. V. (2013). How big data is shaping us health care.

McKinsey Quarterly(2), 17-17. Retrieved from http://www.mckinsey.com/

Kerr, D. J., Knox, K., Robertson, D. C., Stewart, D., & Watson, R. (2008). Clinical trials

explained : A guide to clinical trials in the nhs for healthcare professionals.

Chichester, GBR: Wiley.

Kibby, M. (2011). Patient recruitment feasibility. Applied Clinical Trials, 20(6), 80-86.

Retrieved from http://www.appliedclinicaltrialsonline.com

Lau, J. K., Lowres, N., Neubeck, L., Brieger, D. B., Sy, R. W., Galloway, C. D., . . .

Freedman, S. B. (2013). Iphone ecg application for community screening to detect

135

silent atrial fibrillation: A novel technology to prevent stroke. International

Journal of Cardiology. doi:10.1016/j.ijcard.2013.01.220

Leedy, P. D., & Ormrod, J. E. (2005). Practical research: Planning and design. Upper

Saddle River, N.J.: Prentice Hall.

Lefebvre, K., Anderson, T., Herbertson, K., Keirsey, A., Wnorowski, H., & Palombaro,

K. M. (2010). The use of health related quality of life measurement in

cardiovascular and pulmonary physical therapy practice: An exploratory study.

Cardiopulmonary Physical Therapy Journal, 21(4), 5-13. Retrieved from

http://www.cpptjournal.org/

Lieback, A., Proff, J., Wessel, K., Fleck, E., & Gotze, S. (2012). Remote monitoring of

heart failure patients using implantable cardiac pacing devices and external

sensors: Results of the insight-hf study. Clinical Research in Cardiology, 101(2),

101-107. doi:10.1007/s00392-011-0369-1

Liebo, M. J., Israel, R. L., Lillie, E. O., Smith, M. R., Rubenson, D. S., & Topol, E. J.

(2011). Is pocket mobile echocardiography the next-generation stethoscope? A

cross-sectional comparison of rapidly acquired images with standard transthoracic

echocardiography. Annals of Internal Medicine, 155(1), 33-38. Retrieved from

http://annals.org

Louis, A. A., Turner, T., Gretton, M., Baksh, A., & Cleland, J. G. F. (2003). A systematic

review of telemonitoring for the management of heart failure. European Journal

of Heart Failure, 5(5), 583. doi:10.1016/s1388-9842(03)00160-0

136

Mann, C. J. (2003). Observational research methods. Research design II: Cohort, cross

sectional, and case-control studies. Emergency Medicine Journal(20), 54-60.

doi:10.1136/emj.20.1.54

Maric, B., Kaan, A., Ignaszewski, A., & Lear, S. A. (2009). A systematic review of

telemonitoring technologies in heart failure. European Journal of Heart Failure,

11(5), 506-517. doi:10.1093/eurjhf/hfp036

McHenry, J., Insel, K. C., Einstein, G. O., Vidrine, A. N., Koerner, K. M., & Morrow, D.

G. (2012). Recruitment of older adults: Success may be in the details.

Gerontologist(August). doi:10.1093/geront/gns079

MedApps. (2012). MedApps remote health monitoring. Retrieved from

http://medapps.com

Menachemi, N., Matthews, M., Ford, E. W., Hikmet, N., & Brooks, R. G. (2009). The

relationship between local hospital information technology capabilities and

physician emr adoption. Journal of Medical Systems, 33(5), 329-335. Retrieved

from

http://www.springer.com/statistics/life+sciences,+medicine+%26+health/journal/

10916

Meystre, S. M., Friedlin, F. J., South, B. R., Shen, S., & Samore, M. H. (2010).

Automatic de-identification of textual documents in the electronic health record:

A review of recent research. BMC Medical Research Methodology, 10, 70-85.

doi:10.1186/1471-2288-10-70

137

Mor, V., Intrator, O., Feng, Z., & Grabowski, D. C. (2010). The revolving door of

rehospitalization from skilled nursing facilities. Health Affairs, 29(1), 57-64.

doi:10.1377/hlthaff.2009.0629

Muller, A., Schweizer, J., Helms, T. M., Oeff, M., Sprenger, C., & Zugck, C. (2010).

Telemedical support in patients with chronic heart failure: Experience from

different projects in germany. International Journal of Telemedicine &

Applications, 1-11. doi:10.1155/2010/181806

Mulvany, C. (2009). Preventable readmissions: A prime target for reform. Healthcare

Financial Management: Journal Of The Healthcare Financial Management

Association, 63(9), 32-34. Retrieved from http://www.hfma.org/

Murad, K., & Kitzman, D. W. (2012). Frailty and multiple comorbidities in the elderly

patient with heart failure: Implications for management. Heart Failure Reviews,

17(4-5), 581-588. doi:10.1007/s10741-011-9258-y

Nangalia, V., Prytherch, D. R., & Smith, G. B. (2010). Health technology assessment

review: Remote monitoring of vital signs--current status and future challenges.

Crit Care, 14(5), 233. doi:10.1186/cc9208

National Library of Medicine. (2002). The Hippocratic oath. Retrieved from

http://www.nlm.nih.gov/hmd/greek/greek_oath.html

Nutting, P. A., Crabtree, B. F., Miller, W. L., Stewart, E. E., Stange, K. C., & Jaén, C. R.

(2010). Journey to the patient-centered medical home: A qualitative analysis of

the experiences of practices in the national demonstration project. Annals of

Family Medicine, 8(S1), S45-S56. Retrieved from http://www.annfammed.org/

138

Otani, K., Waterman, B., & Dunagan, W. C. (2012). Patient satisfaction: How patient

health conditions influence their satisfaction. Journal of Healthcare Management,

57(4), 276-292. Retrieved from http://www.ache.org/pubs/jhmtoc.cfm

Pare, G., Moqadem, K., Pineau, G., & St-Hilaire, C. (2010). Clinical effects of home

telemonitoring in the context of diabetes, asthma, heart failure and hypertension:

A systematic review. Journal Of Medical Internet Research, 12(2).

doi:10.2196/jmir.1357

Patrick, J. R. (2001). Net attitude: What it is, how to get it, and why your company can't

survive without it. Cambridge MA: Perseus Publishing.

Petkar, S., Cooper, P., & Fitzpatrick, A. P. (2006). How to avoid a misdiagnosis in

patients presenting with transient loss of consciousness. Post Graduate Medical

Journal, 82(972), 630-641. doi:10.1136/pgmj.2006.046565

Piterman, L., Zimmet, H., Krum, H., Tonkin, A., & Yallop, J. (2005). Chronic heart

failure: Optimising care in general practice. Australian Family Physician, 34(7),

547-553. doi:10.2267/0300-8495.34.7.1826

Polisena, J., Tran, K., Cimon, K., Hutton, B., McGill, S., Palmer, K., & Scott, R. E.

(2010). Home telemonitoring for congestive heart failure: A systematic review

and meta-analysis. Journal of Telemedicine and Telecare, 16(2), 68-76.

doi:10.1258/jtt.2009.090406 Retrieved from http://jtt.rsmjournals.com/

Pollonini, L., Rajan, N. O., Xu, S., Madala, S., & Dacso, C. C. (2012). A novel handheld

device for use in remote patient monitoring of heart failure patients-design and

preliminary validation on healthy subjects. Journal of Medical Systems, 36(2),

653-659. doi:10.1007/s10916-010-9531-y

139

Pranati. (2010). Informed consent: Are we doing enough? Perspectives In Clinical

Research, 1(4), 124-127. doi:10.4103/2229-3485.71769

Ramani, G. V., Uber, P. A., & Mehra, M. R. (2010). Chronic heart failure: Contemporary

diagnosis and management. Mayo Clinic Proceedings, 85(2), 180-195.

doi:10.4065/mcp.2009.0494

Rau, J. (2013). Armed with bigger fines, medicare to punish 2,225 hospitals for excess

readmissions. Kaiser Health News. Retrieved from

http://www.kaiserhealthnews.org/Stories/2013/August/02/readmission-penalties-

medicare-hospitals-year-two.aspx

Reiner, B. (2008). Automating radiologist workflow part 1: The digital consultation. J

Am Coll Radiol, 5(10), 1080-1085. doi:10.1016/j.jacr.2008.05.014

Roberto, J. C., Ferrer, R. L., Miller, W. L., Palmer, R. F., Wood, R., Davila, M., . . .

Stang, K. C. (2010). Patient outcomes at 26 months in the patient-centered

medical home national demonstration project. Annals Of Family Medicine, 8(S1),

S57-S67. doi:10.1370/afm.1121

Rodriguez-Artalejo, F., Guallar-Castillon, P., Pascual, C. R., Otero, C. M., Montes, A. O.,

Garcia, A. N., . . . Herrera, M. C. (2005). Health-related quality of life as a

predictor of hospital readmission and death among patients with heart failure.

Archives of Internal Medicine, 165(11), 1274-1279.

doi:10.1001/archinte.165.11.1274

Roger, V. L. (2010). The heart failure epidemic. International Journal of Environmental

Research and Public Health, 7(4), 1807-1830. Retrieved from

http://www.mdpi.com/journal/ijerph

140

Roger, V. L., & Turner, L. (2011). Heart disease and stroke statistics--2012 update.

Circulation, 2012(125), e12-e230. doi:10.1161/CIR.0b013e31823ac046

Rothman, S. A., Laughlin, J. C., Seltzer, J., Walia, J. S., Baman, R. I., Siouff, S. Y., . . .

Kowey, P. R. (2007). The diagnosis of cardiac arrhythmias: A prospective multi-

center randomized study comparing mobile cardiac outpatient telemetry versus

standard loop event monitoring. Journal of Cardiovascular Electrophysiology,

18(3), 1-7. doi:10.1111/j.1540-8167.2006.00729.x

Saarel, E. V., Doratotaj, S., & Sterba, R. (2008). Initial experience with novel mobile

cardiac outpatient telemetry for children and adolescents with suspected

arrhythmia. Congenital Heart Disease, 3(1), 33-38. doi:10.1111/j.1747-

0803.2007.00162.x

Scherr, D., Kastner, P., Kollmann, A., Hallas, A., Auer, J., Krappinger, H., . . . Fruhwald,

M. F. (2009). Effect of home-based telemonitoring using mobile phone

technology on the outcome of heart failure patients after an episode of acute

decompensation: Randomized controlled trial. Journal Of Medical Internet

Research, 11(3), e34. doi:10.2196/jmir.1252

Seto, E., Leonard, K. J., Cafazzo, J. A., Barnsley, J., Masino, C., & Ross, H. J. (2012).

Perceptions and experiences of heart failure patients and clinicians on the use of

mobile phone-based telemonitoring. Journal Of Medical Internet Research, 14(1),

e25. doi:10.2196/jmir.1912 Retrieved from http://jmir.org

Shafazand, M., Patel, H., Ekman, I., Swedberg, K., & Schaufelberger, M. (2012). Patients

with worsening chronic heart failure who present to a hospital emergency

141

department require hospital care. BMC Research Notes, 5(132).

doi:10.1186/1756-0500-5-132

Sharma, U., Barnett, J., & Clarke, M. (2010). Clinical users’ perspective on

telemonitoring of patients with long term conditions: Understood through

concepts of giddens’s structuration theory & consequence of modernity. Studies

in Health Technology and Informatics, 160(1), 545-549. doi:10.3233/978-1-

60750-588-4-545

Shi, L., & Singh, D. A. (2011). The nation's health (8th ed.). Sudbury, MA: Jones &

Bartlett Learning.

Shugarman, L. R., & Whitenhill, K. (2011). The affordable care act proposes new

provisions to build a stronger continuum of care. Generations, 35(1), 11-18.

Retrieved from http://www.generationsjournal.org/

Singh, B., Russell, S. D., & Cheng, A. (2012). Update on device technologies for

monitoring heart failure. Current Treatment Options in Cardiovascular

Medicine(August). doi:10.1007/s11936-012-0192-7

Slyer, J. T., Concert, C. M., Eusebio, A. M., Rogers, M. E., & Singleton, J. (2011). A

systematic review of the effectiveness of nurse coordinated transitioning of care

on readmission rates for patients with heart failure. JBI Library of Systematic

Reviews, 9(15), 464-490. Retrieved from

http://connect.jbiconnectplus.org/JBIReviewsLibrary.aspx

Smith, A. C. (2013). Effect of telemonitoring on re-admission in patients with congestive

heart failure. Medsurg Nursing, 22(1), 39-44. Retrieved from

http://www.medsurgnursing.net/cgi-bin/WebObjects/MSNJournal.woa

142

Sochalski, J., Jaarsma, T., Krumholz, H. M., Laramee, A., McMurray, J. J., Naylor, M.

D., . . . Stewart, S. (2009). What works in chronic care management: The case of

heart failure. Health Affairs, 28(1), 179-189. Retrieved from

http://www.healthaffairs.org/

Sprint. (2012). Coverage check. Retrieved from

http://coverage.sprintpcs.com/IMPACT.jsp

Stichler, J. F. (2011). Patient-centered healthcare design. Journal of Nursing

Administration, 41(12), 503-506. doi:10.1097/NNA.0b013e3182378a3b

Stoyanov, N., & Paul, V. (2012). Clinical use of telemonitoring in chronic heart failure:

Keeping up with the times or misuse of time? Curr Heart Fail Rep, 9(1), 75-80.

doi:10.1007/s11897-011-0074-4

Straub, C., Haas, A., & Mex, J. (2006). Telemedicine in heart disease: Role of remote

patient management in guideline-based heart failure care. Techniker

Krankenkasse. Retrieved from http://www.tk.de/tk/tk/english/145048

Stream, G. (2012). Investments in medical home model starting to pay dividends. Annals

of Family Medicine, 10(1), 80-81. doi:10.1370/afm.1357

Swanson, M. E., Cowan, J. M., & Blake, R. (2011). Preparation for stage 1 meaningful

use attestation as an eligible hospital. ANIA-CARING Newsletter, 26(2), 10-16.

Retrieved from http://www.ania-

caring.org/mc/page.do?sitePageId=118358&orgId=car

Taylor, J. H. (2012). Ways to improve patient satisfaction. Radiologic Technology, 83(6),

618-619. Retrieved from http://www.radiologictechnology.org

143

Telemedicine credentialing and privileging: Medicare and medicaid programs: Changes

affecting hospital and critical access hospital conditions of participation: Final

rule, 76 C.F.R. (2011).

Thokala, P., Baalbaki, H., Brennan, A., Pandor, A., Stevens, J. W., Gomersall, T., . . .

Wong, R. (2013). Telemonitoring after discharge from hospital with heart failure:

Cost-effectiveness modelling of alternative service designs. BMJ Open, 3(9),

e003250-e003250. doi:10.1136/bmjopen-2013-003250

Tibaldi, V., Isaia, G., Scarafiotti, C., Gariglio, F., Zanocchi, M., Bo, M., . . . Ricauda, N.

A. (2009). Hospital at home for elderly patients with acute decompensation of

chronic heart failure: A prospective randomized controlled trial. Archives of

Internal Medicine, 169(17), 1569-1575. doi:10.1001/archinternmed.2009.267

Tomlinson, M., Rotheram-Borus, M. J., Swartz, L., & Tsai, A. C. (2013). Scaling up

mhealth: Where is the evidence? PLoS Medicine, 10(2), e1001382.

doi:10.1371/journal.pmed.1001382

Tompkins, C., & Orwat, J. (2010). A randomized trial of telemonitoring heart failure

patients. Journal Of Healthcare Management / American College Of Healthcare

Executives, 55(5), 312-322. Retrieved from

http://www.ache.org/pubs/jhmsub.cfm

Topol, E. J. (2012). The creative destruction of medicine : How the digital revolution will

create better health care. New York, NY: Basic Books.

Tucker, P. (2009). Be your own big brother. Futurist, 43(1), 9-9. Retrieved from

http://www.wfs.org/futurist

144

Univeristy of California at Los Angeles. (2012). The bluetooth standard. Retrieved from

http://www.ee.ucla.edu/~lerong/ee202a/hw2/

U.S. Department of Health & Human Services. (2012). Federal policy for the protection

of human subjects ('common rule'). Retrieved from

http://www.hhs.gov/ohrp/humansubjects/commonrule/index.html

U.S. Department of Health & Human Services. (2012). Health information policy.

Retrieved from http://www.hhs.gov/ocr/privacy/

U.S. Department of Health and Human Services. National Heart Lung and Blood

Institute. (2012). People science health. Retrieved from

http://www.hrsa.gov/ruralhealth/about/telehealth/

U.S. Department of Health and Human Services. Health Resources and Services

Administration. (2012). Telehealth. Retrieved from

http://www.hrsa.gov/ruralhealth/about/telehealth/

VanLare, J., & Conway, P. H. (2012). Value-based purchasing--national programs to

move from volume to value. New England Journal of Medicine, 367(4), 292-295.

doi:10.1056/NEJMp1204939

Vasamreddy, C., Dalal, D., Dong, J., Cheng, A., Spragg, D., Lamiy, S. Z., . . . Calkins, H.

(2006). Symptomatic and asymptomatic atrial fibrillation in patients undergoing

radiofrequency catheter ablation. Journal of Cardiovascular Electrophysiology,

17(2), 134-139. doi:10.1111/j.1540-8167.2006.00359.x

Vest, J. R., Gamm, L. D., Oxford, B. A., Gonzalez, M. I., & Slawson, K. M. (2010).

Determinants of preventable readmissions in the United States: A systematic

review. Implementation Science, 5(1), 88. doi:10.1186/1748-5908-5-88

145

Vincent, C., Reinharz, D., Deaudelin, I., Garceau, M., & Talbot, L. R. (2007).

Understanding personal determinants in the adoption of telesurveillance in elder

home care by community health workers. Journal of Community Practice, 15(3),

99. Retrieved from http://www.haworthpress.com/journals/dds.asp

Virtual Private Network Consortium. (2012). VPN protocols. Retrieved from

http://www.vpnc.org/vpn-standards.html

Wade, M. J., Desai, A. S., Spettell, C. M., Snyder, A. D., McGowan-Stackewicz, V.,

Kummer, P. J., . . . Krakauer, R. S. (2011). Telemonitoring with case management

for seniors with heart failure. American Journal of Managed Care, 17(3), e71-

e79. Retrieved from http://www.ajmc.com/

Whellan, D., Ousdigian, K. T., Al-Khatib, S. M., Pu, W., Sarkar, S., Porter, C. B., . . .

O'Connor, C. M. (2010). Combined heart failure device diagnostics identify

patients at higher risk of subsequent heart failure hospitalizations: Results from

partners hf (program to access and review trending information and evaluate

correlation to symptoms in patients with heart failure) study. J Am Coll Cardiol.,

55(17), 1803-1810. doi:10.1016/j.jacc.2009.11.089

Whitten, P., & Mickus, M. (2007). Home telecare for copd/chf patients: Outcomes and

perceptions. Journal of Telemedicine and Telecare, 13(2), 69-73. Retrieved from

http://jtt.rsmjournals.com

Yun-Hee, J., Kraus, S. G., Jowsey, T., & Glasgow, N. J. (2010). The experience of living

with chronic heart failure: A narrative review of qualitative studies. BMC Health

Services Research, 10, 77-65. doi:10.1186/1472-6963-10-77

146

Zanaboni, P., & Wootton, R. (2012). Adoption of telemedicine: From pilot stage to

routine delivery. BMC Medical Informatics & Decision Making, 12(1), 1-9.

doi:10.1186/1472-6947-12-1

Zhang, W., & Watanabe-Galloway, S. (2008). Ten-year secular trends for congestive

heart failure hospitalizations: An analysis of regional differences in the United

States. Congestive Heart Failure, 14(5), 266-271. doi:10.1111/j.1751-

7133.2008.00009.x

Zigmond, J. (2012). Innovation won't wait. Despite law's uncertainty, CMS awards

grants. Modern Healthcare, 42(20), 12. Retrieved from

http://www.modernhealthcare.com

147

Author Biography

John Patrick is President of Attitude LLC and former vice president of Internet

technology at IBM, where he worked for thirty-eight years. John was a founding member

of the World Wide Web Consortium at MIT in 1994, a founding member and past

chairman of the Global Internet Project, a member of the Internet Society and the

American College of Healthcare Executives, a senior member of the Association for

Computing Machinery, and a Fellow of the Institute of Electrical and Electronics

Engineers. John is a board member at Mediabistro Inc. and the Online Computer Library

Center, and is a member of the WesConn Biomedical Research Institute Advisory

Council. John is the author of Net Attitude (Patrick, 2001). John holds a BS degree in

electrical engineering from Lehigh University, an MS in management from the

University of South Florida, and an LLB from LaSalle Extension University.