Healthcare 2.0: The Age of Analytics

© 2013 Health Catalystwww.healthcatalyst.com


The Age of AnalyticsHealthcare 2.0:

Dale Sanders, Aug 2013


Overview

2

Key Principl

es

• Every Industry: How Data Management Evolves

• Preserving Agility: Analytic Data Binding

• The Triple Aim: Closed Loop Analytics

• The Roadmap: Healthcare Analytic Adoption Model Ev

aluating Option

s

• Strategy & Vendor Options

• A Checklist: Population Health Management

Organi

zationa

l Issues • ACO Data

Acquisition Timeline

• Data Governance

Time Allowing: Health Catalyst Screen Shots

© 2013 Health Catalystwww.healthcatalyst.com3

Evolution of Data ManagementEvery industry follows the same pattern

Data Collection1

2 Data Sharing

3 Data Analysis

Billing, Radiology and Lab systems; EMRs, etc.

Local Area Networks, Health Information Exchanges

Enterprise Data Warehouses (EDW)


Who or what are we

monitoring?

What are our goals?

What are we measuring?

How will we achieve them?

Data and Technology

Organization and Culture

Regularly return to these fundamentalsThe Four Questions of Analytics


55

Atomic data must be “bound” to business rules about that data and to vocabularies related to that data in order to create information

Vocabulary binding in healthcare is pretty obvious● Unique patient and provider identifiers● Standard facility, department, and revenue center codes● Standard definitions for gender, race, ethnicity● ICD, CPT, SNOMED, LOINC, RxNorm, RADLEX, etc.

Examples of binding data to business rules● Length of stay● Patient relationship attribution to a provider● Revenue (or expense) allocation and projections to a department● Revenue (or expense) allocation and projections to a physician● Data definitions of general disease states and patient registries● Patient exclusion criteria from disease/population management● Patient admission/discharge/transfer rules

Binding Data to Create Information


Data Binding

Vocabulary

“systolic &diastolicblood pressure”

Rules

“normal”

Pieces ofmeaningless

data

11560

Bindsdata to

Software Programming


Why Is This Concept Important?

Two tests for tight, early binding

Knowing when to bind data, and howtightly, to vocabularies and rules is

THE KEY to analytic success and agility

Is the rule or vocabulary widely accepted as true and accurate in the organization or industry?

Comprehensive Agreement

Is the rule or vocabulary stable and rarely change?

PersistentAgreement

Acknowledgements to Mark Beyer of Gartner


ACADEMIC

STATE

SOURCEDATA CONTENT

SOURCE SYSTEMANALYTICS

CUSTOMIZED DATA MARTS

DATAANALYSIS

OTHERS

HR

FINANCIAL

CLINICAL

SUPPLIES

INTE

RN

AL

EX

TER

NA

L

ACADEMIC

STATE

OTHERS

HR

FINANCIAL

CLINICAL

SUPPLIES

RESEASRCH REGISTRIES

QlikView

Microsoft Access/ODBC

Web applications

Excel

SAS, SPSS

Et al

OPERATIONAL EVENTS

CLINICAL EVENTS

COMPLIANCE AND PAYER MEASURES

DISEASE REGISTRIES

MATERIALS MANAGEMENT

3

Data Rules and Vocabulary Binding Points

High Comprehension & Persistence of vocabulary & business rules? => Early binding

Low Comprehension and Persistence of vocabulary or business rules? => Late binding

Six Points to Bind Data in an Analytic System

421 5 6


Words of CautionHealthcare suffers from a low degree of Comprehensive and Persistent agreement on many topics that impact analytics

The vast majority of vendors and home grown data warehouses bind to rules and vocabulary too early and too tightly, in comprehensive enterprise data models

Analytic agility and adaptability suffers greatly• “We’ve been building our EDW for two years.”• “I asked for that report last month.”

Closed Loop Analytics: The Triple Aim Google: “Intermountain antibiotic assistant”


Center of the Universe Shifts

11

EMR

Analytics& EDW

Analytics& EDW

EMR

CopernicusPtolemy


Healthcare Analytic Adoption ModelLevel 8 Cost per Unit of Health Reimbursement &

Prescriptive AnalyticsContracting for & managing health. Customizing patient care based on population outcomes.

Level 7 Cost per Capita Reimbursement & Predictive Analytics

Diagnosis-based financial reimbursement, managing risk proactively, measuring true outcomes

Level 6 Cost per Case Reimbursement& The Triple Aim

Procedure-based financial risk and applying “closed loop” analytics at the point of care

Level 5 Clinical Effectiveness & Population Management Measuring & managing evidence based care

Level 4 Automated External Reporting Efficient, consistent production; agility, and governance

Level 3 Automated Internal Reporting Efficient, consistent production; widespread access to KPIs

Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data

Level 1 Integrated, Enterprise Data Warehouse Foundation of data and technology

Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth

Level 8Cost per Unit of Health Reimbursement & Prescriptive Analytics: Providers Analytic motive expands to wellness management and mass customization of care. Physicians, hospitals, employers, payers and members/patients collaborate to share risk and reward (e.g., financial reward to patients for healthy behavior). Analytics expands to include NLP of text, prescriptive analytics, and interventional decision support. Prescriptive analytics are available at the point of care to improve patient specific outcomes based upon population outcomes. Data content expands to include genomic and familial information. The EDW is updated within a few minutes of changes in the source systems.

Level 7Cost per Capita Reimbursement & Predictive Analytics: Analytic motive expands to address diagnosis-based, fixed-fee per capita reimbursement models. Focus expands from management of cases to collaboration with clinician and payer partners to manage episodes of care, using predictive modeling, forecasting, and risk stratification to support outreach, triage, escalation and referrals. Patients are flagged in registries who are unable or will not participate in care protocols. Data content expands to include external pharmacy data and protocol-specific patient reported outcomes. On average, the EDW is updated within one hour or less of source system changes.

Level 6Cost per Case Reimbursement & The Triple Aim: The “accountable care organization” shares in the financial risk and reward that is tied to clinical outcomes. At least 50% of acute care cases are managed under bundled payments. Analytics are available at the point of care to support the Triple Aim of maximizing the quality of individual patient care, population management, and the economics of care. Data content expands to include bedside devices and detailed activity based costing. Data governance plays a major role in the accuracy of metrics supporting quality-based compensation plans for clinicians and executives. On average, the EDW is updated within one day of source system changes. The EDW reports organizationally to a C-level executive who is accountable for balancing cost of care and quality of care.

Level 5

Clinical Effectiveness & Population Management: Analytic motive is focused on measuring clinical effectiveness that maximizes quality and minimizes waste and variability. Data governance expands to support care management teams that are focused on improving the health of patient populations. Permanent multidisciplinary teams are in-place that continuously monitor opportunities to improve quality, and reduce risk and cost, across acute care processes, chronic diseases, patient safety scenarios, and internal workflows. Precision of registries is improved by including data from lab, pharmacy, and clinical observations in the definition of the patient cohorts. EDW content is organized into evidence-based, standardized data marts that combine clinical and cost data associated with patient registries. Data content expands to include insurance claims. On average, the EDW is updated within one week of source system changes.

Level 4Automated External Reporting: Analytic motive is focused on consistent, efficient production of reports required for regulatory and accreditation requirements (e.g. CMS, Joint Commission, tumor registry, communicable diseases); payer incentives (e.g. MU, PQRS, VBP, readmission reduction); and specialty society databases (e.g. STS,NRMI, Vermont-Oxford). Adherence to industry-standard vocabularies is required. Clinical text data content is available for simple key word searches. Centralized data governance exists for review and approval of externally released data.

Level 3Automated Internal Reporting: Analytic motive is focused on consistent, efficient production of reports supporting basic management and operation of the healthcare organization. Key performance indicators are easily accessible from the executive level to the front-line manager. Corporate and business unit data analysts meet regularly to collaborate and steer the EDW. Data governance expands to raise the data literacy of the organization and develop a data acquisition strategy for Levels 4 and above.

Level 2Standardized Vocabulary & Patient Registries: Master vocabulary and reference data identified and standardized across disparate source system content in the data warehouse. Naming, definition, and data types are consistent with local standards. Patient registries are defined solely on ICD billing data. Data governance forms around the definition and evolution of patient registries and master data management.

Level 1Integrated, Enterprise Data Warehouse: At a minimum, the following data are co-located in a single data warehouse, locally or hosted: HIMSS EMR Stage 3 data, Revenue Cycle, Financial, Costing, Supply Chain, and Patient Experience. Searchable metadata repository is available across the enterprise. Data content includes insurance claims, if possible. Data warehouse is updated within one month of changes in the source system. Data governance is forming around the data quality of source systems. The EDW reports organizationally to the CIO.

Level 0Fragmented Point Solutions: Vendor-based and internally developed applications are used to address specific analytic needs as they arise. The fragmented Point Solutions are neither co-located in a data warehouse nor otherwise architecturally integrated with one another. Overlapping data content leads to multiple versions of analytic truth. Reports are labor intensive and inconsistent. Data governance is non-existent.

©

Healthcare Analytic Adoption Model


The ACO Data Acquisition ChecklistBilling data1

Lab data2

Imaging data3

Inpatient EMR data4

Outpatient EMR data5

Claims Data6

HIE Data7

Detailed cost accounting8

Bedside monitoring data9

External pharmacy data10

Familial data11

Home monitoring data12

Patient reported outcomes data13

Long term care facility data14

Genomic data15

Real-time 7x24 biometric monitoring for all patients in the ACO16

NO

W1-

2 Y

EA

RS

2-4

YE

AR

S



Evaluating Options

15

16

Strategy and Analytic OptionsStrategy Option

Pros & Cons Example Vendors

Buy & Build from an Analytics Platform Vendor

Highest degree of analytic flexibility and adaptability Requires a data driven culture with high aspirations that views analytics as a clear

business differentiator Best suited for a culture with a higher degree of data literacy and data

management skills Slow initial time-to-value plagues some vendors Inconsistent ROI track record, but when ROI occurs, it’s big

Caradigm Intelligence Platform Health Catalyst Healthcare Data Works IBM Healthcare Data Model Oracle Healthcare Data Model Recombinant (Deloitte)

Buy from an Analytics Service Provider

Best suited for cultures that want to avoid the details of analytics and data management, but aspire to improve basic internal and external reporting

Inter-organizational benchmarking and comparative analytics is a natural part of the business model and service

Limited analytic flexibility and adaptability Substantive ROI is not well-documented nor widely acknowledged

Explorys Humedica Lumeris Premier Alliance Truven Analytics Suite

Buy “Best of Breed” Point Solutions

Leverages expertise and very specific analytics applications in business and clinical areas that are not always available in other options

Does not facilitate data integration; i.e., does not provide a single analytic perspective on patient care and costs

Costly and complicated to maintain

AltaSoft Crimson Suite EPSI MedeAnalytics Medventive Midas Omincell

Buy from your EMR Vendor

Offers the possibility of “closed loop analytics” driving analytics back to the point of care, in the EMR and clinical workflow

No proven track record with analytics to date from the EMR vendors Tend to be very focused on analytics that are specific to the EMR vendor’s data Less flexible and adaptable to new sources of data and analytic use cases,

especially complex ones

Allscripts Sunrise Cerner PowerInsight Epic Clarity & Cogito McKesson Horizon Meditech Data Repository Siemens Decision Support

Population Health Management A Checklist for Requirements and Functionality

1. Evidence based definitions of patients to include in the PHM registries

2. Clinician-patient attribution algorithms

3. Discrete, evidence based methods for flagging patients in the registries that are difficult to manage in the protocol, or should be excluded from the registry, altogether

4. Evidence based triage and clinical protocols for single disease states

5. Evidence based triage and clinical protocols for comorbid patients

6. Metrics and monitoring of clinical effectiveness and total cost of care (to the system and the patient)

7. Risk stratified work queues for outreach that feed care management teams and processes

8. Access to test results and medication compliance data outside the core healthcare delivery organization

9. Patient engagement and communication system about their care, including coordination of benefits

10. Patient education material and a distribution system, tailored to their status and protocol

11. Patient reported outcomes measurement system, tailored to their status and protocol

12. Inter-physician/clinician communication system about overlapping patients


Data GovernanceGovern to the least extent required for the common good

Base your committee charter on…

Encouraging more, not less, data access

Increasing data contentin the EDW

Campaigning fordata literacy Resolving analytic priorities

Enhancing data quality Establishing standards for Master Reference Data


In Summary• Analytics is the R in the ROI of IT investments

• This is a new chance for healthcare to do the right thing, the first time, with IT strategy and investments

• Unlike the bumpy road of EMR adoption

• C-level executives need to be highly literate in data management

• You can’t delegate all the details. The consequences to the business are too significant.

• All industries now move at the speed of software and analytics• Test for Comprehensive and Persistent understanding

• Follow the curriculum of the Analytic Adoption Model• Be deliberate and stick to the fundamentals


Thank You!• Questions?

• Contact information• [email protected]• [email protected]• @drsanders• www.linkedin.com/in/dalersanders

mailto:[email protected]




https://www.linkedin.com/in/dalersanders





Catalyst Screen Shots

21


Key Process Analysis

Identifying Variability

Opportunities For Waste Reduction and Quality Improvement


Hospital and Clinical Operations


Executive Dashboard


Population Explorer

On The Path To

Population Health Management


Population Health Management


The Age of AnalyticsTaking the next step…• Download the Late-Binding Data

Warehouse White Paper

www.healthcatalyst.com/LateBindingWhitePaper

• Download the Analytics Adoption Model White Paperwww.healthcatalyst.com/analytics-adoption-model

• Contact us to learn more about our solutions and communication tools

www.healthcatalyst.com/company/contact-us

http://pages.healthcatalyst.com/rs/healthcatalyst/images/HC%20Late%20Binding%20White%20Paper.pdf

http://www.healthcatalyst.com/wp-content/uploads/2013/11/analytics-adoption-model-Nov-2013.pdf

http://pages.healthcatalyst.com/contact-us.html

Healthcare 2.0: The Age of Analytics

Health & Medicine

Transcript of Healthcare 2.0: The Age of Analytics