Developing Population Health Clinical Informatics System Requirements to Support Primary Care Delivery and Quality Improvement Developing Population Health Clinical Informatics System Requirements to Support Primary Care Delivery and Quality Improvement Brian Arndt MD Lawrence Hanrahan PhD MS Jonathan Temte MD PhD Marc Hansen MD George Mejicano MD MPH John Frey MD David Simmons MPH November 7, 2008

2 Presentation Objectives 1.Review the basics of medical informatics and its domains. 2.Review progress to date on early collaborations in clinical informatics between the UW DFM and the WI Department of Public Health. 3.Review population health informatics including best practices in data analysis. 4.Explore ways in which health information technology can build a critical bridge between primary care and the public health care system.

Medical Informatics Definition: the systematic application of computer science and technology to medical practice, research, and medical educationDefinition: the systematic application of computer science and technology to medical practice, research, and medical education Scope includes the conceptualization, design, development, deployment, refinement, maintenance, and evaluation of systems relevant to medicineScope includes the conceptualization, design, development, deployment, refinement, maintenance, and evaluation of systems relevant to medicine

Medical Informatics Domains Bioinformatics Molecular Cellular Genetic Adapted from Shortliffe Imaging Informatics Tissues Organs Clinical Informatics Individual Patients Public Health Informatics Population Health

Medical Informatics Hierarchy Bioinformatics Imaging Informatics Clinical Informatics Public Health Informatics Adapted from Shortliffe

Illustration: Public Health Alerts & Reporting Introductory statement printed each week in Public Health Reports, :Introductory statement printed each week in Public Health Reports, : “No health department, state or local, can effectively prevent or control disease without knowledge of when, where, and under what conditions cases are occurring.” Despite being mandated by law, communicable disease reporting is poor – incomplete, inaccurate, and delayedDespite being mandated by law, communicable disease reporting is poor – incomplete, inaccurate, and delayed 6

The current state of the art … DIARRHEA ALERT VIA 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/ diarrhea → Patient w/ diarrhea

New Clinical Information Systems Needed! We’ve officially arrived at the point of no return!

Clinical and Population Health Informatics Diffusion Model Data Collection Clinical Informatics Two Way Information Flow Clinical Systems Population Health Data Collection Data Interpretation Data Interpretation Data Analysis Data Analysis Information Dissemination

Public Health Information Flow 10 Wisconsin Department of Health Services WI Dept of Public Health EMR DataCentral ServerEMR Alerts practice alert in EMR if patient presents with symptoms matching condition !

Public Health Alerts through the EMR (Legionella Outbreak)

12 Informed, Activated Patient Productive Interactions Prepared, Proactive Practice Team Delivery System Design Decision Support Clinical Information Systems Self- Management Support Health System Resources & Policies Community Health Care Organization Chronic Care Model Improved Outcomes

13 Clinical Information Systems ID chronic conditions that require both proactive and reactive careID chronic conditions that require both proactive and reactive care Diabetes, CHF, asthma, metabolic syndrome, etcDiabetes, CHF, asthma, metabolic syndrome, etc The conditions to follow often are dictated by larger systems (ie, local health plans with Pay for Performance programs)The conditions to follow often are dictated by larger systems (ie, 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 diabetes –Hyperlipidemia → Coronary artery disease –Elevated BP w/o HTN → HTN –Overweight → Obesity Develop algorithms to appropriately identify patientsDevelop algorithms to appropriately identify patients –Billing data is usually not enough – consider addition of lab data, prescription medication data, EMR problem list abstraction, etc

14 Develop Registries Organize clinic subpopulation data to plan quality improvement efforts and to facilitate new care processesOrganize clinic subpopulation data to plan quality improvement efforts and to facilitate new care processes –What about determining comorbidity score (ie, Charlson) from administrative data to target patients at highest risk of mortality? Many EMRs are adequate for managing individuals, but cannot manage populations wellMany EMRs are adequate for managing individuals, but cannot manage populations well –Practices should think about this functionality when purchasing an EMR Registries can be created in the absence of a fully functional EMR with other commonly available softwareRegistries can be created in the absence of a fully functional EMR with other commonly available software –Microsoft Excel, Microsoft Access, etc –Physicians Plus Insurance Corp. currently uses DocSite –What are the algorithms insurers use to identify our patients with a particular condition?

15 DFM Diabetes Registry

16 Develop Registries Assess performance of individual patients and clinicians, clinical teams, clinics, health systems, and ultimately communitiesAssess performance of individual patients and clinicians, clinical teams, clinics, health systems, and ultimately communities –Provide regular (and accurate!) feedback for continuous quality improvement –Reports can be generated to document trends (both improvements and setbacks) –Target appropriate clinical interventions based on analysis outcomes using Plan-Do-Study-Act (PDSA) cycles

Population Health Informatics Data Analysis Best Practices (Example: Diabetes patients with A1c >7) Analysis TypeExampleUtility 1 – Case series60% in clinic have A1c >7Lowest 2 – Simple comparisonClinic rate of 60% is higher than statewide rate of 50% Low 3 – Comparison + TestClinic 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 Data Analysis Best Practices There is limited understanding of disease burden or its risk factors without formal testing, data tables, charts, graphs, and maps to display variationThere is limited understanding of disease burden or its risk factors without formal testing, data tables, charts, graphs, and maps to display variation To create meaningful disease burden displays, each assessment must be compared to an appropriate referenceTo create meaningful disease burden displays, each assessment must be compared to an appropriate reference The comparison must be tested (p value, Confidence Interval, Relative Risk)The comparison must be tested (p value, Confidence Interval, Relative Risk) Ideally, other known predictors of risk must be controlled when comparisons are madeIdeally, other known predictors of risk must be controlled when comparisons are made

Clinic A1c > 7 Rate or Relative Risk – Age, Gender, & Race Adjusted (95% Confidence Intervals) Rate RRRate RRR StateUS2020 Clinic B Clinic A Clinic D Clinic E Clinic C Risk Compared to State, US, and 2020 Target

Interpretation of Color Gradients CountyColorResultInterpretation Clinic ARank highest – Significant from referent Disease disparity Clinic BRank high – Not significant from referent Possible disease disparity - cautious monitoring Clinic CRank same as referent – Not significant Possible disease disparity with room to improve - cautious monitoring Clinic DRank low – Not significantHealth advantage - hopeful monitoring possible Clinic ERank lowest – SignificantHealth advantage

Population Health Informatics Data Analysis Best Practices: A Proposed v1.0 When the goal is identifying disparity and prediction of risk, it is appropriate to use automated computer selection algorithms (ie, backward elimination) built into computer packages 1When the goal is identifying disparity and prediction of risk, it is appropriate to use automated computer selection algorithms (ie, backward elimination) built into computer packages 1 Multiple factors are examined and their simultaneous, independent contribution to health is determinedMultiple factors are examined and their simultaneous, independent contribution to health is determined The Wisconsin Public Health Information Network (PHIN) Analysis, Visualization, and Reporting (AVR) system makes this possibleThe Wisconsin Public Health Information Network (PHIN) Analysis, Visualization, and Reporting (AVR) system makes this possible 1 Source: Kleinbaum, Logistic Regression (1994)

The Wisconsin PHIN AVR Portal

The UW DFM Pilot De-identified visit records were provided from the Epic EMR over a 1 year period (N = 309,000)De-identified visit records were provided from the Epic EMR over a 1 year period (N = 309,000) Secure, role-based access controlSecure, role-based access control

Data Cube (Structured data for efficient exploration)

Population Health Charting Example Acute respiratory infections, Pneumonia and influenza AND Temperature ≥100 AND Service Year 2007

Geographic Information System (GIS): Diabetes Visit Count by Zip Code

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

Diabetes Use Case Proposal: Population Health Multivariate Model to Support Primary Care & QI Outcomes =Patient Factors + Clinician Factors + Clinic Factors + Community Factors Obesity Hypertension Depression Diabetes CVD Smoking Alcohol A1c level LDL HDL BP Diet Phys Activity Process factors (ie, time to repeat follow-up) Age Gender Race/ethnicity Co-morbidities Medications Literacy Culture Psycho- demographics Insurance Census block / tract / zipcode Age Gender Certifications Specialty Graduation date Years of practice Location Capabilities Processes Census block / tract / zipcode: Poverty Education level Psycho-demographics (ie, purchasing habits) Built environment: Traffic Recreation / parks Sidewalks Restaurant mix Safety / crime Fast food sales Fresh fruit & vegetable sales / consumption

UW Clinical Informatics Evolution Our next steps to develop population health informatics requirements:Our next steps to develop population health informatics requirements: –Core work group will continue literature review & refine proposal –Focus groups convening 1/9/2009 & 1/16/2009 Develop paper prototypeDevelop paper prototype –RSS / wider distribution & feedback / CME? –Pilot testing (starting 7/1/2009) Please consider joining us!Please consider joining us!