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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 Presentation Objectives 1. 2. 3. 4. Review the basics of medical informatics and its domains. Review progress to date on early collaborations in clinical informatics between the UW DFM and the WI Department of Public Health. Review population health informatics including best practices in data analysis. Explore ways in which health information technology can build a critical bridge between primary care and the public health care system. 2 Medical Informatics • Definition: 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 medicine Medical Informatics Domains Bioinformatics Molecular Cellular Genetic Imaging Informatics Clinical Informatics Public Health Informatics Tissues Organs Individual Patients Population Health Adapted from Shortliffe Medical Informatics Hierarchy Public Health Informatics Clinical Informatics Imaging Informatics Bioinformatics Adapted from Shortliffe Illustration: Public Health Alerts & Reporting • Introductory statement printed each week in Public Health Reports, 1913-1951: “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 delayed 6 The current state of the art … DIARRHEA ALERT VIA EMAIL CHAIN: Amanda Kita (Public Health) → Mike Holman (UWMF Employee Health) → Sue Kaletka (DFM Administration) → Mark Shapleigh (Clinic Manager) → Brian Arndt (Clinician) → 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 Interpretation Data Analysis Clinical Systems Data Collection Data Interpretation Information Dissemination Population Health Data Collection Clinical Informatics Two Way Information Flow Data Analysis Public Health Information Flow Wisconsin Department of Health Services EMR Data Central Server WI Dept of Public Health ! EMR Alerts practice alert in EMR if patient presents with symptoms matching condition 10 Public Health Alerts through the EMR (Legionella Outbreak) Chronic Care Model Community Resources & Policies Health System Health Care Organization SelfManagement Support Informed, Activated Patient Delivery System Design Decision Support Productive Interactions Clinical Information Systems Prepared, Proactive Practice Team Improved Outcomes 12 Clinical Information Systems • ID chronic conditions that require both proactive and reactive care • Diabetes, CHF, asthma, metabolic syndrome, etc • 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 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 patients – Billing data is usually not enough – consider addition of lab data, prescription medication data, EMR problem list abstraction, etc 13 Develop Registries • Organize 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 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 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? 14 DFM Diabetes Registry 15 Develop Registries • Assess 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 16 Population Health Informatics Data Analysis Best Practices (Example: Diabetes patients with A1c >7) Analysis Type Example 1 – Case series 60% in clinic have A1c >7 2 – Simple comparison Clinic rate of 60% is higher than statewide rate of 50% 3 – Comparison + Test Clinic rate of 60% is significantly higher than statewide rate of 50% Utility Lowest Low Medium 4 – Adjusted comparison Age adjusted clinic rate of 60% is + Test (ie, adjust for significantly higher than statewide principal determinant) age adjusted rate of 50% Higher 5 – Multivariate model + Test (ie, adjust for all important risk factors / determinants) Highest Clinic rate of 60% adjusted for age, gender, race, and insurance status is significantly higher than comparably adjusted statewide rate of 50% 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 variation • To 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) • Ideally, 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) Clinic A R a t e Clinic B Clinic C Clinic D R R Clinic E Risk Compared to State, US, and 2020 Target Interpretation 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 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 packages1 • Multiple 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 possible 1Source: 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) • Secure, role-based access control Data Cube (Structured data for efficient exploration) Population Health Charting Example Acute respiratory infections, 480-487 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 + 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 Psychodemographics Insurance Census block / tract / zipcode Age Location Gender Capabilities Certifications Processes Specialty Graduation date Years of practice Community Factors 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: – Core work group will continue literature review & refine proposal – Focus groups convening 1/9/2009 & 1/16/2009 • Develop paper prototype – RSS / wider distribution & feedback / CME? – Pilot testing (starting 7/1/2009) • Please consider joining us!