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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 Why Understanding Population SegmentsIs the Future of Stratification Matthew MitchellData Analytics ManagerCentral City Concern
Risk stratification … and its limits Population segmentation … and why it matters Vision for the future 1 2 3
14,000 people experience homelessness in Multnomah County each year 2017 estimate
Top 5% High Risk Middle 6%-20% Rising Risk Lower 80% Low Risk Example of risk stratification pyramid
100 of 3,403 patients selected (2.9%) Risk stratification tool, HealthShare of Oregon
Dozens of questions become just three categories? Example scoring of VI-SPDAT
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
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
Deliver the right services to the right people Need stratification, not risk stratification
Poverty Discrimination Trauma Toxic Stress Increasing needs Life Progression Central City Concern’s Population Segmentation Framework
Older, sicker, complex needs Younger, healthier, less complex needs Central City Concern’s Population Segmentation Framework
Older, sicker, complex needs Younger, healthier, less complex needs Central City Concern’s Population Segmentation Framework
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
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
Some subgroups have high hospital utilization lo hi lo hi lo hi lo lo hi hi Central City Concern’s Population Segmentation Framework
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
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
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
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
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%
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%
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%
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
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
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
Allows more time to: • Build relationships • Outreach • Provide timely support • Increase access to team • Smooth transitions of care Summit team care model
Reducing Expensive Utilization 53% Decrease in ED visits Results of targeted Summit intervention
Reducing Expensive Utilization 43% Decrease in inpatientadmissions Results of targeted Summit intervention
$16,000 Net cost savings to health systemper patient per year Estimated cost savings of Summit intervention
This is not about Cost Savings
This is not about Algorithms or Analytics
This is not about Innovative Uses of Data
