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Beyond Risk Stratification

Beyond Risk Stratification. Why Understanding Population Segments Is the Future of Stratification Matthew Mitchell Data Analytics Manager Central City Concern. Risk stratification … and its limits Population segmentation … and why it matters Vision for the future. 1. 2. 3.

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Beyond Risk Stratification

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  1. Beyond Risk Stratification Why Understanding Population SegmentsIs the Future of Stratification Matthew MitchellData Analytics ManagerCentral City Concern

  2. Risk stratification … and its limits Population segmentation … and why it matters Vision for the future 1 2 3

  3. First, a bit about Portland…

  4. Local news on homelessness

  5. Google Trends – News

  6. Google Trends – News

  7. Local Estimate & Point-In-Time Count, HUD

  8. American Community Survey 2016

  9. Housing Inventory Count, HUD

  10. 14,000 people experience homelessness in Multnomah County each year 2017 estimate

  11. Where I’m coming from…

  12. Skid Row, Los Angeles 2007

  13. Boston 2010 – 2015

  14. Risk stratification … and its limits

  15. High utilizers in the media and research

  16. Top 5% High Risk Middle 6%-20% Rising Risk Lower 80% Low Risk Example of risk stratification pyramid

  17. 100 of 3,403 patients selected (2.9%) Risk stratification tool, HealthShare of Oregon

  18. Popular assessment and screening tools

  19. Dozens of questions become just three categories? Example scoring of VI-SPDAT

  20. Reduce multiple dimensions to only one dimension? Safford, Monika M., Jeroan J. Allison, and Catarina I. Kiefe. "Patient complexity: more than comorbidity. The vector model of complexity." Journal of General Internal Medicine 22.3 (2007): 382-390. Examples of vector model of complexity

  21. Mitchell, Matthew S., et al. "Cost of health care utilization among homeless frequent emergency department users.“ Psychological services 14.2 (2017): 193. High utilizers meet population segmentation

  22. Population Segmentation

  23. Deliver the right services to the right people Need stratification, not risk stratification

  24. Poverty Discrimination Trauma Toxic Stress Increasing needs Life Progression Central City Concern’s Population Segmentation Framework

  25. Older, sicker, complex needs Younger, healthier, less complex needs Central City Concern’s Population Segmentation Framework

  26. Older, sicker, complex needs Younger, healthier, less complex needs Central City Concern’s Population Segmentation Framework

  27. High Complexity Schizophrenia Bipolar andTrauma Trauma andDepression Alcohol Use andDepression Opioid Use andHepatitis C Stimulant Use andDepression Low Complexity Central City Concern’s Population Segmentation Framework

  28. High Complexity Schizophrenia Medical Bipolar Trauma Medical Trauma Depression Medical Depression Alcohol Medical Opioid Medical Stimulant Depression Medical Schizophrenia Stimulant Bipolar Trauma Trauma Depression SUD Depression Alcohol Opioid Hep C Stimulant Depression Low Complexity Central City Concern’s Population Segmentation Framework

  29. Some subgroups have high hospital utilization lo hi lo hi lo hi lo lo hi hi Central City Concern’s Population Segmentation Framework

  30. High Complexity lo hi Bipolar andTrauma Trauma andDepression Alcohol Use andDepression Opioid Use andHepatitis C Stimulant Use andDepression Schizophrenia lo hi lo hi lo lo hi hi Low Complexity

  31. Validating the Framework

  32. Chan, Brian, Mitchell Matthew, and Dorr, David. “Predicting Risk of Hospitalization in a Healthcare for the Homeless Population Using Population Segments and Artificial Neural Network Models.” Journal of General Internal Medicine (2018) 33(Suppl 2): 83. Poster at Society of General Internal Medicine Annual Meeting

  33. Future Hospitalization Base Predictors Age Housing Status Income Medical Diagnoses Psychiatric Diagnoses Substance Use Emergency Department Medical Admissions Psychiatric Admissions Completed Appointments No Show Appointments Base prediction model

  34. Population Segments Future Hospitalization Base Predictors Age Housing Status Income Medical Diagnoses Psychiatric Diagnoses Substance Use Emergency Department Medical Admissions Psychiatric Admissions Completed Appointments No Show Appointments Prediction model augmented with population segments

  35. Receiver operating curve

  36. Receiver operating curve

  37. High Complexity 1% 4% 22% Average predicted risk of hospitalization Bipolar andTrauma Trauma andDepression Alcohol Use andDepression Opioid Use andHepatitis C Stimulant Use andDepression Schizophrenia 1% 1% 9% 1% 1% 9% 4% 2% 6% 1% 0% 5% 4% 1% 10% Low Complexity 1%

  38. High Complexity 1% 4% 22% Average predicted risk of hospitalization Bipolar andTrauma Trauma andDepression Alcohol Use andDepression Opioid Use andHepatitis C Stimulant Use andDepression Schizophrenia 1% 1% 9% 1% 1% 9% 4% 2% 6% 1% 0% 5% 4% 1% 10% Low Complexity 1%

  39. High Complexity 1% 4% 22% Average predicted risk of hospitalization Start the Ignition Presents: Bipolar andTrauma Trauma andDepression Alcohol Use andDepression Opioid Use andHepatitis C Stimulant Use andDepression Schizophrenia 3WeirdReasons Population Segmentation Actually Matters 1% 1% 9% 1% 1% 9% 4% 2% 6% 1% 0% 5% 4% 1% 10% YES NO I want to know more I don’t really care Low Complexity 1%

  40. 3 Weird Reasons Population Segmentation Actually Matters Homelessness isn’t a thing—it’s a range of different experiences 1 2 Different people have different needs Interventions are only as successful as the targeting strategy 3

  41. Population Segmentation in Practice

  42. High Complexity lo hi Bipolar andTrauma Trauma andDepression Alcohol Use andDepression Opioid Use andHepatitis C Stimulant Use andDepression Schizophrenia lo hi lo hi lo lo hi hi Low Complexity

  43. High Complexity lo hi Bipolar andTrauma Trauma andDepression Alcohol Use andDepression Opioid Use andHepatitis C Stimulant Use andDepression Schizophrenia lo hi lo hi lo lo hi hi Low Complexity

  44. Allows more time to: • Build relationships • Outreach • Provide timely support • Increase access to team • Smooth transitions of care Summit team care model

  45. Reducing Expensive Utilization 53% Decrease in ED visits Results of targeted Summit intervention

  46. Reducing Expensive Utilization 43% Decrease in inpatientadmissions Results of targeted Summit intervention

  47. $16,000 Net cost savings to health systemper patient per year Estimated cost savings of Summit intervention

  48. This is not about Cost Savings

  49. This is not about Algorithms or Analytics

  50. This is not about Innovative Uses of Data

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