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Population health analytics - Chris Morris

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Making Data Work For You

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Population health analytics - Chris Morris

  1. 1. Population Health Analytics Chris Morris Principal Analyst Development & Modelling Analytics
  2. 2. Overview • Current – Adjusted Clinical Groups (ACGs) • Future - Integrated Population Analytics (IPA) • Risk Stratification – beyond predicting Emergency Admissions • Population Analytics – segmentation and other approaches • Summary 2
  3. 3. Current – Adjusted Clinical Groups (ACGs) • Suite of tools including; – Predictive models – Descriptive units for Case-mix comparison (e.g. multi- morbidity groups) • 2 UK recalibrations • Historic use - Emergency Admissions DES • Value of combined Primary and Secondary Care dataset not fully realised for commissioners – Data Sharing Agreement only for Risk Stratification – Additional use subject to lengthy permission process – Few organisational drivers for further uses 3
  4. 4. Future – Integrated Population Analytics (IPA) • Incorporates ACGs but flexible to include other tools (Frailty, Mortality etc.) • Twin Cores: – Risk engine – Pseudonymous Data Warehouse • Data Sharing Agreements make explicit that data can be used for commissioning analysis • Many organisational drivers for these uses – NMOCs, STPs, ACOs, Vanguards 4
  5. 5. Integrated Population Analytics (IPA) 5
  6. 6. Current vs. Future focus Service lens Patient and population lens
  7. 7. Risk Stratification: Beyond Predicting Emergency Admissions 7
  8. 8. Emergency Admissions 8 1) Emergency Admissions are rare events 2) Hard to predict
  9. 9. Persistent High Cost (Top 20% next 3 6-mth periods) 9 1) Less rare 2) Easier to predict
  10. 10. Total Healthcare Cost >£1,000 10 1) Even less rare 2) Even easier to predict
  11. 11. Population Analytics: Segmentation and Other Approaches 11
  12. 12. Segmentation (Age & Multi-morbidity) 12 Analytically meaningful – move from snapshot analysis to BAU commissioning analytics
  13. 13. Segmentation (Specific Disease Combinations) 13 Clinically meaningful…
  14. 14. Segmentation (by Defined Health Outcomes) 14 ‘Interventionally’ meaningful…
  15. 15. Benchmarking - understanding variation
  16. 16. Future contracts Actuarial… Regression based… 16
  17. 17. Whole system modelling 17 NHSE Channel Shift Model
  18. 18. Whole system modelling 18 Discrete event simulation
  19. 19. Monitoring and evaluation 19 Matched controls approach (York CHE)
  20. 20. Data Science Opportunities 20 Process mining example (Prodel M et al 2015): derives composite pathway
  21. 21. Summary • The future is bright! • Risk prediction will become more sophisticated • Population Analytics will become much more prominent 21
  22. 22. Thank you Sarah.scobie@nhs.net Chris.morris2@nhs.net 22

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