Developing Population Health Developing Population Health Clinical Informatics System Clinical Informatics System

Requirements to Support Primary Requirements to Support Primary Care Delivery and Quality Care Delivery and Quality

Improvement Improvement

Brian Arndt MD Brian Arndt MD Lawrence Hanrahan PhD MS Lawrence Hanrahan PhD MS

Jonathan Temte MD PhD Jonathan Temte MD PhD Marc Hansen MD Marc Hansen MD

George Mejicano MD MPHGeorge Mejicano MD MPHJohn Frey MDJohn Frey MD

David Simmons MPHDavid Simmons MPH

November 7, 2008November 7, 2008

2

Presentation ObjectivesPresentation Objectives

1.1. Review the basics of medical informatics and Review the basics of medical informatics and its domains.its domains.

2.2. Review progress to date on early collaborations Review progress to date on early collaborations in clinical informatics between the UW DFM in clinical informatics between the UW DFM and the WI Department of Public Health.and the WI Department of Public Health.

3.3. Review population health informatics including Review population health informatics including best practices in data analysis.best practices in data analysis.

4.4. Explore ways in which health information Explore ways in which health information technology can build a critical bridge between technology can build a critical bridge between primary care and the public health care system.primary care and the public health care system.

Medical InformaticsMedical Informatics

• Definition: the systematic application of Definition: the systematic application of computer science and technology to computer science and technology to medical practice, research, and medical medical practice, research, and medical educationeducation

• Scope includes the conceptualization, Scope includes the conceptualization, design, development, deployment, design, development, deployment, refinement, maintenance, and evaluation refinement, maintenance, and evaluation of systems relevant to medicineof systems relevant to medicine

Medical Informatics DomainsMedical Informatics Domains

Bioinformatics

Molecular CellularGenetic

Adapted from Shortliffe

Imaging Informatics

TissuesOrgans

Clinical Informatics

Individual Patients

Public HealthInformatics

PopulationHealth

Medical Informatics HierarchyMedical Informatics Hierarchy

Bioinformatics

Imaging Informatics

Clinical Informatics

Public Health Informatics

Adapted from Shortliffe

Illustration: Public Health Alerts & Illustration: Public Health Alerts & ReportingReporting

• Introductory statement printed each week Introductory statement printed each week in in Public Health ReportsPublic Health Reports, 1913-1951:, 1913-1951:

““No health department, state or local, can No health department, state or local, can effectively prevent or control disease effectively prevent or control disease

without knowledge of when, where, and without knowledge of when, where, and under what conditions cases are occurring.”under what conditions cases are occurring.”

• Despite being mandated by law, Despite being mandated by law, communicable disease reporting is poor – communicable disease reporting is poor – incomplete, inaccurate, and delayedincomplete, inaccurate, and delayed

6

The current state of the art …The current state of the art …

DIARRHEA ALERT VIA EMAIL CHAINDIARRHEA ALERT VIA EMAIL CHAIN:: Amanda Kita (Public Health) Amanda Kita (Public Health) → → Mike Holman (UWMF Employee Health)Mike Holman (UWMF Employee Health) → → Sue Kaletka (DFM Administration) Sue Kaletka (DFM Administration) → → Mark Shapleigh (Clinic Manager)Mark Shapleigh (Clinic Manager) → → Brian Arndt (Clinician) Brian Arndt (Clinician) → → Patient w/ diarrheaPatient w/ diarrhea

New Clinical Information Systems New Clinical Information Systems Needed!Needed!

We’ve officially arrived at the point of no return!

Clinical and Population Health Clinical and Population Health InformaticsInformatics Diffusion ModelDiffusion Model

DataCollection

Clinical Informatics Two Way Information Flow

Clinical Systems

PopulationHealt

h

DataCollection

DataInterpretation

DataInterpretation

DataAnalysis

DataAnalysis

Information Dissemination

Public Health Information FlowPublic Health Information Flow

10

  Wisconsin Department of Health ServicesWI Dept of Public Health

EMR Data

Central Server

EMR Alerts

practice alert in EMR if patient presents with

symptoms matching condition

!

Public Health Alerts through the Public Health Alerts through the EMREMR

(Legionella Outbreak)

12

Informed,Activated

Patient

ProductiveInteractions

Prepared,Proactive

Practice Team

DeliverySystemDesign

DecisionSupport

ClinicalInformation

Systems

Self-Management

Support

Health System

Resources & Policies

Community

Health Care Organization

Chronic Care ModelChronic Care Model

Improved Outcomes

13

Clinical Information SystemsClinical Information Systems

• ID chronic conditions that require both proactive and ID chronic conditions that require both proactive and reactive carereactive care

• Diabetes, CHF, asthma, metabolic syndrome, etcDiabetes, CHF, asthma, metabolic syndrome, etc• The conditions to follow often are dictated by larger systems (ie, The conditions to follow often are dictated by larger systems (ie,

local health plans with Pay for Performance programs)local health plans with Pay for Performance programs)

• Also consider conditions that may progress furtherAlso consider conditions that may progress further– Impaired fasting glucose or gestational diabetes → Type 2 diabetesImpaired fasting glucose or gestational diabetes → Type 2 diabetes– Hyperlipidemia → Coronary artery diseaseHyperlipidemia → Coronary artery disease– Elevated BP w/o HTN → HTNElevated BP w/o HTN → HTN– Overweight → ObesityOverweight → Obesity

• Develop algorithms to appropriately identify patientsDevelop algorithms to appropriately identify patients– Billing data is usually not enough – consider addition of lab data, Billing data is usually not enough – consider addition of lab data,

prescription medication data, EMR problem list abstraction, etcprescription medication data, EMR problem list abstraction, etc

14

Develop RegistriesDevelop Registries

• Organize clinic subpopulation data to plan quality Organize clinic subpopulation data to plan quality improvement efforts and to facilitate new care improvement efforts and to facilitate new care processesprocesses– What about determining comorbidity score (ie, Charlson) from What about determining comorbidity score (ie, Charlson) from

administrative data to target patients at highest risk of administrative data to target patients at highest risk of mortality?mortality?

• Many EMRs are adequate for managing individuals, Many EMRs are adequate for managing individuals, but cannot manage populations wellbut cannot manage populations well– Practices should think about this functionality when Practices should think about this functionality when

purchasing an EMRpurchasing an EMR

• Registries can be created in the absence of a fully Registries can be created in the absence of a fully functional EMR with other commonly available functional EMR with other commonly available softwaresoftware– Microsoft Excel, Microsoft Access, etcMicrosoft Excel, Microsoft Access, etc– Physicians Plus Insurance Corp. currently uses DocSitePhysicians Plus Insurance Corp. currently uses DocSite– What are the algorithms insurers use to identify our patients What are the algorithms insurers use to identify our patients

with a particular condition? with a particular condition?

15

DFM Diabetes RegistryDFM Diabetes Registry

16

Develop RegistriesDevelop Registries

• Assess performance of individual patients and Assess performance of individual patients and clinicians, clinical teams, clinics, health systems, clinicians, clinical teams, clinics, health systems, and ultimately communitiesand ultimately communities– Provide regular (and accurate!) feedback for continuous Provide regular (and accurate!) feedback for continuous

quality improvementquality improvement– Reports can be generated to document trends (both Reports can be generated to document trends (both

improvements and setbacks)improvements and setbacks)– Target appropriate clinical interventions based on Target appropriate clinical interventions based on

analysis outcomes using Plan-Do-Study-Act (PDSA) cyclesanalysis outcomes using Plan-Do-Study-Act (PDSA) cycles

Population Health InformaticsPopulation Health InformaticsData Analysis Best PracticesData Analysis Best Practices

(Example: Diabetes patients with A1c (Example: Diabetes patients with A1c >7)>7)

Analysis Type Example Utility

1 – Case series 60% in clinic have A1c >7 Lowest

2 – Simple comparison Clinic rate of 60% is higher than statewide rate of 50%

Low

3 – Comparison + Test Clinic rate of 60% is significantly higher than statewide rate of 50%

Medium

4 – Adjusted comparison + Test (ie, adjust for principal determinant)

Age adjusted clinic rate of 60% is significantly higher than statewide age adjusted rate of 50%

Higher

5 – Multivariate model + Test (ie, adjust for all important risk factors / determinants)

Clinic rate of 60% adjusted for age, gender, race, and insurance status is significantly higher than comparably adjusted statewide rate of 50%

Highest

Population Health Informatics Population Health Informatics Data Analysis Best PracticesData Analysis Best Practices

• There is limited understanding of disease burden There is limited understanding of disease burden or its risk factors without formal testing, data or its risk factors without formal testing, data tables, charts, graphs, and maps to display tables, charts, graphs, and maps to display variation variation

• To create meaningful disease burden displays, To create meaningful disease burden displays, each assessment must be compared to an each assessment must be compared to an appropriate referenceappropriate reference

• The comparison must be tested (p value, The comparison must be tested (p value, Confidence Interval, Relative Risk)Confidence Interval, Relative Risk)

• Ideally, other known predictors of risk must be Ideally, other known predictors of risk must be controlled when comparisons are madecontrolled when comparisons are made

Clinic A1c > 7 Rate or Relative Clinic A1c > 7 Rate or Relative Risk – Risk –

Age, Gender, & Race Adjusted Age, Gender, & Race Adjusted (95% Confidence Intervals)(95% Confidence Intervals)

Rate

RR

State US 2020

Clinic B

Clinic A

Clinic D

Clinic E

Clinic C

Risk Compared to State, US, and 2020 Target

Interpretation of Color GradientsInterpretation of Color Gradients

County Color Result Interpretation

Clinic A Rank highest – Significant from referent

Disease disparity

Clinic B Rank high – Not significant from referent

Possible disease disparity - cautious monitoring

Clinic C Rank same as referent – Not significant

Possible disease disparity with room to improve -cautious monitoring

Clinic D Rank low – Not significant Health advantage - hopeful monitoring possible

Clinic E Rank lowest – Significant Health advantage

Population Health InformaticsPopulation Health InformaticsData Analysis Best Practices: A Data Analysis Best Practices: A

Proposed v1.0Proposed v1.0

• When the goal is identifying disparity and When the goal is identifying disparity and prediction of risk, it is appropriate to use prediction of risk, it is appropriate to use automated computer selection algorithms (ie, automated computer selection algorithms (ie, backward elimination) built into computer backward elimination) built into computer packagespackages1 1

• Multiple factors are examined and their Multiple factors are examined and their simultaneous, independent contribution to health simultaneous, independent contribution to health is determinedis determined

• The Wisconsin Public Health Information The Wisconsin Public Health Information Network (PHIN) Analysis, Visualization, and Network (PHIN) Analysis, Visualization, and Reporting (AVR) system makes this possibleReporting (AVR) system makes this possible

11Source: Kleinbaum, Logistic Regression (1994)Source: Kleinbaum, Logistic Regression (1994)

The Wisconsin PHIN AVR PortalThe Wisconsin PHIN AVR Portal

The UW DFM PilotThe UW DFM Pilot

• De-identified visit records were provided from the De-identified visit records were provided from the Epic EMR over a 1 year period (N = 309,000)Epic EMR over a 1 year period (N = 309,000)

• Secure, role-based access controlSecure, role-based access control

Data Cube Data Cube (Structured data for efficient (Structured data for efficient

exploration)exploration)

Population Health Charting ExamplePopulation Health Charting ExampleAcute respiratory infections, 480-487Acute respiratory infections, 480-487

Pneumonia and influenza AND Temperature Pneumonia and influenza AND Temperature ≥100 AND Service Year 2007≥100 AND Service Year 2007

Geographic Information System Geographic Information System (GIS):(GIS):

Diabetes Visit Count by Zip CodeDiabetes Visit Count by Zip Code

Stored Analytic Process:Stored Analytic Process:Logistic Regression Modeling Diabetes Logistic Regression Modeling Diabetes Risk Predicted by Age and Body Mass Risk Predicted by Age and Body Mass

IndexIndex

Diabetes Use Case Proposal:Diabetes Use Case Proposal:Population Health Multivariate Model to Population Health Multivariate Model to

Support Primary Care & QI Support Primary Care & QI

Outcomes = Patient Factors +

ClinicianFactors +

ClinicFactors +

CommunityFactors

Obesity HypertensionDepressionDiabetesCVDSmokingAlcoholA1c levelLDLHDLBPDietPhys Activity Process factors(ie, time to repeat follow-up)

AgeGenderRace/ethnicityCo-morbiditiesMedicationsLiteracyCulturePsycho-demographicsInsurance Census block / tract / zipcode

AgeGenderCertifications SpecialtyGraduation dateYears of practice

LocationCapabilitiesProcesses

Census block / tract / zipcode:

PovertyEducation levelPsycho-demographics (ie, purchasing habits)

Built environment:

TrafficRecreation / parks SidewalksRestaurant mix Safety / crimeFast food salesFresh fruit & vegetable sales / consumption

UW Clinical Informatics EvolutionUW Clinical Informatics Evolution

• Our next steps to develop population health Our next steps to develop population health informatics requirements:informatics requirements:– Core work group will continue literature review & Core work group will continue literature review &

refine proposalrefine proposal– Focus groups convening 1/9/2009 & 1/16/2009Focus groups convening 1/9/2009 & 1/16/2009

• Develop paper prototypeDevelop paper prototype

– RSS / wider distribution & feedback / CME?RSS / wider distribution & feedback / CME?– Pilot testing (starting 7/1/2009)Pilot testing (starting 7/1/2009)

• Please consider joining us!Please consider joining us!