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Harnessing the power of Big Data to take a new tact: Population Health

Harnessing the power of Big Data to take a new tact: Population Health. Jeffrey Murawsky, MD FACP Regional Chief Executive Officer (Director) Great Lakes Veterans Integrated Service Network (VISN 12). February 16, 2014. Defining Population Health.

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Harnessing the power of Big Data to take a new tact: Population Health

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  1. Harnessing the power of Big Data to take a new tact: Population Health Jeffrey Murawsky, MD FACP Regional Chief Executive Officer (Director) Great Lakes Veterans Integrated Service Network (VISN 12) February 16, 2014

  2. Defining Population Health Health outcomes of a group of individuals, including the distribution of such outcomes Public Health Clinical Medicine Governmental activities to prevent disease, promote health, and prolong life among the population as a whole Diagnosis and treatment of disease and the maintenance of health Population Health

  3. Determinants of Population Health Factors outside of the medical care system account for about 80% of population health status.

  4. A Larger View of Health Systems

  5. 1995/96 2003/04 Heart Attack Patients in Large Health Regions – Treatment and 30 Day Mortality Rates (%) – 1995/96 to 2003/04: A cautionary tale from Canada

  6. Healthy People 2020 • General health status (life expectancy, healthy life expectancy; physically and mentally unhealthy days; self-assessed health status; limitation of activity; chronic disease prevalence) • Health-related quality of life and well-being (measures of physical, mental, and social health-related quality of life; well-being and satisfaction; participation in common activities); • Determinants of health (biology, genetics, individual behavior, access to health services, and the environment in which people are born, live, learn, play, work, and age); and • Disparities (measures of differences in health status associated with race and ethnicity, gender, physical and mental ability, and geography).

  7. VA’s Population Health Program • Performance typically measured for all patients, not just those who happen to visit • Model population-based programs • Outreach to OEF/OIF/OND veterans • Social support programs • Vet Centers • Homeless, HUD/VASH • Care-coordination for high-risk patients • CAN/PCAS • Activities (mostly in VBA) that address social determinants of health (education & housing assistance; vocational rehabilitation, etc) • Health promotion and disease prevention activities, e.g., HCV, HIV, obesity, smoking, depression, PTSD • Efforts to stratify populations by health risk

  8. What will be needed to develop information resources for pop health? • Standardized data of dependable quality • New types of data, especially patient-reported outcomes, health status • Data extracted from text-based sources • Data for other health systems, other gov’t sources • Improved analytic tools

  9. VA Analytic Ecosystem Common Data  Common Infrastructure  Common Tools  Common Security REGION 3 REGION 1 REGION 4 REGION 2 Production VISTA VISTA Analytic Shadow Regional Data Warehouse • CDW System: • VISTA source systems: 130 • Extract tools: • Production Journal Reader • Regional Journal Reader • Batch Extractor • Data facts: • Domains of information: 51 • Rows of data: 80 billion • Columns of data: 20,000 • Tables of data: 840 • Hardware: • 4000+ Processing cores • 1.5 Petabytes storage • Active Users: 20,000 VISN Data Warehouse RPT Vx Vx Vx Vx Vx Vx Vx Vx Vx Vx RPT • Unique patients: 20 million • Outpatient encounters: 1.6 billion • Inpatient admissions: 9 million • Clinical orders: 3.2 billion • Lab tests: 5.6 billion • Pharmacy fills: 1.5 billion • Radiology procedures: 162 million • Vital signs: 2.3 billion • Text notes: 2.0 billion VISN:12-17 VISTAs: 29 VISN:1-5 VISTAs: 28 Vy Vy Vy Vy Vy Vy Vy Vy Vy Vy R4 Rx R1 R3 R2 R4 Rx R1 R2 R3 RPT Vn Vn Vn Vn Vn Vn Vn Vn Vn Vn RPT VISN:18-22 VISTAs: 33 • CDW System Facts: • VISTA source systems: 130 • Extract tools: • Production Journal Reader • Regional Journal Reader • Batch Extractor • Data facts: • Domains of information: 51 • Rows of data: 80 billion • Columns of data: 20,000 • Tables of data: 840 • xDW Hardware Facts: • 4000+ Processing cores • 1.5 Petabytes storage • Active Users: 20,000 Reporting Farm • CDW Sample Data Facts: • Unique patients: 20 million • Outpatient encounters: 1.6 billion • Inpatient admissions: 9 million • Clinical orders: 3.2 billion • Lab tests: 5.6 billion • Pharmacy fills: 1.5 billion • Radiology procedures: 162 million • Vital signs: 2.3 billion • Text notes: 2.0 billion RPT VISN:6-11 VISTAs: 41 Enterprise CDW – Corporate Data Warehouse (Production, Raw, Static, R&D Mirror) HPC Grid – Analytic Compute Grid (SAS, SPSS, R, HADOOP Cluster) RPT – Reporting Farm (SharePoint) Ana Apps – Analytical Applications (Mobile, Web, NLP) App Store – Central store of shareable analytic applications GIS – Geospatial Intelligence System (ESRI) R&D – Clinical and Operational Research (e.g. VINCI Project) 5

  10. CDW “Enclaves” Region 2 VISTA Data Region 1 VISTA Data Non VISTA Data Region 3 VISTA Data Region 4 VISTA Data CDW–General Purpose: CDW-Research: CDW enclave to support Health Services Research & develment (a.k.a. VINCI) CDW-OIA BI: CDW enclave to support OIA Business Intelligence (BI) group (a.k.a. VSSC) CDW-OIA Analytics: CDW enclave to support OIA Analytics

  11. Predicted and Observed Likelihood of Death/Admission 4,505,501 primary care patients C=0.79 C=0.81 C=0.81 C=0.83 C=0.85 C=0.87

  12. 95th %ile – 39% 96th %ile – 42% 97th %ile -- 49% 98th %ile -- 57% 99th %ile -- 72% Patients in highest percentile of risk have 62% probability of admission, 30% probability of death, and 72% probability of either event

  13. Risk Data Updated Weekly • About 1000 web users monthly

  14. Mental Health Diagnoses according to CAN Score

  15. Few Patients with High Scores Referred to Coordination Programs: Telehealth, HBPC, Palliative Care, and Hospice • Palliative Care • Score ≥ 95 --1,353 of 241,917 total patients (0.6%) • Hospice • Score ≥ 95 -- 569 of 241,917 total patients (0.2%)

  16. Total Patientss and Percentage with CAN Score ≥95 by Geographic Area

  17. 2.37% - 9.03% 9.04% - 10.01% 10.02% - 10.96% 10.97% - 12.18% 12.19% - 19.34% 1.86% - 5.93% 5.94% - 7.00% 7.01% - 7.97% 7.98% - 9.21% 9.22% - 16.99% Use of High Level Analytic Data for Population Management and Resource Planning 1-yr likelihood of admission or death 1-yr likelihood of admission 12

  18. DCG Scores by County

  19. Hot Spot Analysis (DCG Scores by County) Significant Cold Spots Significant Hot Spots

  20. Average BMI by County, 2011

  21. % of Patients who are Obese (BMI ≥ 30), 2011

  22. Average Annual Income by County

  23. Adjusted Hazard Ratio (HR) for mortality by neighborhoodSES index decile for 15,889 Veterans Adjusted for age, sex, individual income, race/ethnicity, education, work status, marital status, self-reported health conditions, smoking status, service connected status, and health care access.

  24. Effect of Neighborhood on Health Status Adjusted for age, sex, income, race/ethnicity, education, work status, marital status, self-reported health conditions, smoking status, service connected status, and health care access

  25. Summary • Traditional medical approaches to major health problems are increasingly likely to be unsuccessful – new approaches required • Health delivery systems will be incentivized to maximize health rather than medical care

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