Knowledge Discovery in Databases: Improving Quality in...

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1 Knowledge Discovery in Databases: Improving Quality in Homecare Bonnie L. Westra, PhD, RN, Assistant Professor University of Minnesota, School of Nursing An educational update to the HIMSS Management Engineering – Performance Improvement Task Force June 17, 2008

Transcript of Knowledge Discovery in Databases: Improving Quality in...

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Knowledge Discovery in Databases: Improving Quality in Homecare

Bonnie L. Westra, PhD, RN, Assistant ProfessorUniversity of Minnesota, School of Nursing

An educational update to the HIMSS Management Engineering – Performance Improvement Task Force

June 17, 2008

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Acknowledgments

Co-InvestigatorsKay Savik, MS John H. Holmes, PhD Cristina Oancea, MS, PhD Student (RA)Lynn Choromanski, MS, RN, PhD Student (RA)Mary Dierich, MS, RN, PhD Student

Industrial PartnersCareFacts Information SystemsCHAMP SoftwareDeb Solomon, RN, MS, Home Caring & Hospice (consultant)

FundingUniversity of Minnesota Digital Technology Initiative Grant, UMN-Grant-In-Aide, NIH Health Trajectory –

P20 Grant

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Objectives

Describe current homecare research using EHR data•

Demonstrate a series of steps in comparing traditional statistical analytic methods with knowledge discovery methods (data mining)

Examine lessons learned with the use of EHR data quality improvement

Explore the use of KDD for future research

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Problem

Increasing homecare/ community-based care–

Annual expenditure in 2005 of $47.5 billion

2000 CMS implemented PPS for Medicare patients•

Concern about decrease in service/ visits on outcomes

First study -

28% hospitalization rate nationally –

remained constant

Limited research on ways to reduce hospitalization

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Research Aims

The purpose of the first study was to develop predictive models for risk factors

associated with increased

likelihood of hospitalization

of homecare patients and discover if interventions documented as part of routine care using the Omaha System influence hospitalization.

Use knowledge discovery in databases combined with traditional statistics.

Reported here is the first models using traditional statistics.

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Design/ Sample

Secondary analysis of EHR data •

OASIS and Omaha System interventions from two different EHR systems and 15 homecare agencies.

Data included •

All patients in 2004 receiving homecare services

with a

minimum of two OASIS records for the start and end of an episode of care and who also had Omaha System interventions.

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KDD Process

Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R. Advances in knowledge discovery and data mining. Menlo Park, CA: AAI Press/ The MIT Press Press; 1996.

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Expertise Required

Clinical expert–

What data are collected, when, why, and how–

Interpretation of the data–

Meaningful decisions throughout the process•

Information system knowledge -

specifying requirements

What data are available–

Similarity across agencies and vendors–

Data base issues –

how the data are stored•

Data analysis

Statistical knowledge–

Data mining knowledge•

Clinical validation throughout the process

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OASIS Data

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OMAHA SYSTEM PROBLEMS

ENVIRONMENTAL 22 - Dentition 1 - Income 23 - Cognition 2 - Sanitation 24 - Pain 3 - Residence 25 - Consciousness 4 - Neighborhood/workplace safety 26 - Integument

5 - Other 27 - Neuro-musculo-skeletal function

PSYCHSOCIAL 28 - Respiration 6 - Communication with community resources 29 - Circulation 7 - Social contact 30 - Digestion-hydration 8 - Role change 31 - Bowel function 9 - Interpersonal relationship 32 - Genito-urinary function 10 - Spiritual distress 33 - Antepartum/postpartum 11 - Grief 34 - Other 12 - Emotional stability HEATH RELATED BEHAVIORS 13 - Human sexuality 35 - Nutrition 14 - Caretaking/parenting 36 - Sleep and rest patterns 15 - Neglected child/adult 37 - Physical activity 16 - Abused child/adult 38 - Personal hygiene 17 - Growth and development 39 - Substance use 18 - Other 40 - Family planning PHYSIOLOGICAL 41 - Health care supervision 19 - Hearing 42 - Prescribed medication regimen 20 - Vision 43 - Technical procedure 21 - Speech and language 44 - Other

Omaha System

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Analyses

Traditional statistical analyses–

Frequencies, descriptive, histograms

Chi square/ bivariate association–

Latent class analysis

Logistic regression analysis

Future -

Data mining techniques–

Visualization–

Feature selection–

Decision trees–

Clustering

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Preprocessing

18,067 OASIS records for 3,199 patients–

Missing data

Duplicate records–

Invalid values

989,772 Omaha System Interventions–

Missing data

Matched patients with OASIS and Omaha System Data

65,000 Medication records

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Data Preparation

Preparation –

cleaning data–

Missing values

Duplicate records–

Out of range values

Grouping data into episodes of care

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Unit of Analysis

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Episodes•

2,806 patients -

4,242 Episodes

Discharge, 48.8%

Transfer, 38.6%

Continue, 10.9%

Death, 1.7%

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Transformation

Summative scales•

Prognosis, Pain, Pressure Ulcers, Stasis Ulcers, Surgical Wounds, Respiratory Status, ADLs, IADLs

Clinical Classification Software•

Primary diagnoses and then reduced into 51 smaller groups within

11 major categories

Charlson

Index of Comorbidity•

Additional medical diagnosesInterventions

Theoretically grouped into 23 categoriesCreated dummy variables

For non-normally distributed data

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Primary Diagnoses11

Groups

Categories51

Clinical Classification Software Groups

260

Primary Diagnoses – ICD 9 codes~13,000

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Clinical Classification SoftwareGrouping CCS Categories DescriptorsCardiac and Other Circulation Diseases

24 97, 98, 99, 111, 112, 113, 117, 120, 121

Hypertension & other circulatory diseases

25 100, 101, 102 Myocardial infarction

26 103, 104, 96, 213, 245 Other heart disease

27 105, 106 Conduction

28 108 Congestive Heart Failure; NONHP

29 109, 110 Acute cerebrovascular disease

30 114, 116, 118, 119 Peripheral atherosclerosis

31 115 Aneurysm

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Applying a Clusterer: Identifying similarities and dissimilarities

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Data Analysis

Latent class analysis–

ADL Scale (M0640 –

M0710) –

Who Provides Assistance (M0350) –

Management of medications (M0780) –

Diagnosis group (M0230 CCS Groups)

Logistic regression–

Create models for predictors of hospitalization -

OASIS–

Added interventions –

Omaha System Interventions

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Demographics

2,806 patients Mean age 74.4 (SD = 14.1)–

64.6% Females –

97.9% White

4,242 Episodes–

Length of stay ranged from 1 -

6,354 days (Median = 38 days) –

48.8% discharged –

38.6% transfer to inpatient setting –

1,620 (38.4%) hospitalized–

29.9% continued with care–

1.7% died

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Demographics

Primary diagnoses (most frequent)•

18.8% cardiac and circulatory diseases •

18.1% orthopedic/ trauma surgery and follow up •

9.1% endocrine and nutrition •

7.3% respiratory problems •

2.3% infectious diseases Charlson Index of Comorbidity

0 –

10 with a mean of .58 (SD = 1.32)Interventions (384,081)

62.5% monitoring •

44.9% teaching •

30.2% treatments •

16.0% case management

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Class I: Functionally Impaired

Risk Factors Risk of Hospitalization

Assistance with IADLs 1.5 –

2.3 ↓

Expected Prognosis 1.9 –

2.2 ↑

Charlson Index 2.6 –

3.3 ↑

Medicare as homecare payor 2.0 –

2.3 ↑

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Significant InterventionsVariable Frequency OR

Monitoring Injury Prevention Moderate 1.7 ↑

Significant Interventions Class I: Functionally Impaired

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Class III: Cardiac/ CirculatoryRisk Factors Risk of Hospitalization

IADL Status: 1.5 –

2.3 ↑

Expected Prognosis: 1.6 –

1.8 ↑

Pain 1.9 –

2.2 ↑

Charlson Index 2.1 –

2.6 ↑

Bowel Incontinence 2.0 ↑

Patient equipment 3.9 ↑

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Significant InterventionsVariable Frequency OR

Teaching Disease Treatment Moderate .50 ↓Providing Medication Treatment Low 1.9 ↑Teaching Disease Treatment High 3.0 ↑

Significant Interventions Class III: Cardiac/ Circulatory

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Interpreting ResultsWho Interprets

Nurses on research team•

Homecare clinical manager–

Broader homecare audience

What were they asked?•

Latent Classes –

are they meaningful?•

Within class predictors–

What does it mean to have bowel incontinence as a predictor of hospitalization?•

Across classes: most consistent predictors of hospitalization are –

Charlson Index of Comorbidity, –

Prognosis–

Medicare–

Patient management of equipment–

IADLs

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Discussion

Homecare patients are heterogeneous in needs –

latent class analysis was useful

ADLs, management or oral medications, caregiver assistance, and primary diagnoses

Differences between classes•

Similarities across classes

Most consistent predictors of hospitalization are Charlson Index

of Comorbidity, prognosis, Medicare, patient management of equipment, and IADLs

The addition of interventions to the predictive models for hospitalization modified some predictors -

Injury prevention•

Some interventions were risk factors, others were protective

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Is There a Better Way?

Use KDD methods•

How are they similar or different?

What can we learn compared with traditional statistical analyses?

What are the strengths and weaknesses?

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Definition

Knowledge discovery in databases (KDD)–

Rigorous analytic approach –

Combines traditional statistical concepts with semi-automated analyses

Uses tools from the statistical and machine learning –

Inductive, data driven approach to analyze large, complex datasets–

Identify patterns in data that could be missed using only traditional analytic methods.

Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. Second Edition ed. San Francisco: Morgan Kaufmann; 2005.

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Traditional Statistics KDD

Feature Selection Chi-Square, bivariate Chi-SquareInfoGainCFS evaluation

BestFirst Greedy StepwiseGenetic

Clustering Latent Class K MeansEM

Predictive Modeling Logistic Regression Decision TreesBayesian Network

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Strengths & Weaknesses

Traditional Statistics•

Well known and accepted

Use to discover and test hypotheses•

Limited by statistical assumptions

KDD•

Newer and treated with suspicion

Used for discovery•

Much more flexible in working with data

Requires more interaction in making decisions about data•

Health care data is temporal and non-retangular

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Lessons Learned

Health care data are messy –

audit, Audit, AUDIT!!–

80% is data preparation (minimally)

Know your data –

dwell in the data early and often•

Many decisions made to manage the data –

each could

influence the validity of the results–

Incorrectly coded data–

Missing data–

Data reduction strategies–

Feature selection –

cut points–

Dummy variables –

cut points

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Lessons Learned

Walk before you run–

Phasing in steps with each subsequent study

Comparisons between traditional and data mining techniques

Both use similar math–

Difference in assumptions and how data are managed–

Data mining -

discovery–

Traditional statistics –

discovery & verification

Art and a science

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Research in Process

Predict outcomes using protective / risk factors (OASIS), interventions (Omaha System) and medication data

Hospitalization and emergent care use (DTI)–

Pressure ulcers and incontinence (P20)

Oral medication management/ ambulation (GIA)•

Clustering of interventions

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Bonnie Westra, PhD, RN

Assistant Professor & Co-Director ICNP Center

University of Minnesota, School of Nursing

Robert Wood Johnson, Nurse Executive Fellow5-140 Weaver-Densford Hall

308 Harvard St. SE

Minneapolis, MN 55455

W -

612-625-4470

F -

612-626-3255

[email protected]

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Thank you!

For more information, please contact HIMSS Staff Liaison

JoAnn W. Klinedinst, CPHIMS, PMP, FHIMSS at [email protected]