The document discusses using population health analytics to improve risk stratification and care for patients. It describes the current use of Adjusted Clinical Groups (ACGs) and plans for an integrated population analytics (IPA) approach in the future. The IPA will incorporate ACGs as well as other tools and allow data sharing for broader commissioning analysis. It aims to shift the focus from service-level to the patient and population level through approaches like risk stratification beyond emergency admissions, segmentation of the population, benchmarking, and whole system modeling.

1. Population Health
Analytics
Chris Morris
Principal Analyst
Development & Modelling Analytics

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
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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
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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
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5. Integrated Population Analytics (IPA)
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6. Current vs. Future focus
Service lens Patient and population lens

7. Risk Stratification:
Beyond Predicting Emergency Admissions
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8. Emergency Admissions
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1) Emergency Admissions are rare events
2) Hard to predict

9. Persistent High Cost
(Top 20% next 3 6-mth periods)
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1) Less rare
2) Easier to predict

10. Total Healthcare Cost >£1,000
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1) Even less rare
2) Even easier to predict

11. Population Analytics:
Segmentation and Other Approaches
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12. Segmentation
(Age & Multi-morbidity)
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Analytically meaningful – move from snapshot analysis to BAU commissioning analytics

13. Segmentation
(Specific Disease Combinations)
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Clinically meaningful…

14. Segmentation
(by Defined Health Outcomes)
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‘Interventionally’ meaningful…

15. Benchmarking - understanding variation

16. Future contracts
Actuarial… Regression based…
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17. Whole system modelling
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NHSE Channel Shift Model

18. Whole system modelling
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Discrete event simulation

19. Monitoring and evaluation
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Matched controls approach (York CHE)

20. Data Science Opportunities
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Process mining example (Prodel M et al 2015): derives composite pathway

21. Summary
• The future is bright!
• Risk prediction will become more
sophisticated
• Population Analytics will become much more
prominent
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22. Thank you
Sarah.scobie@nhs.net
Chris.morris2@nhs.net
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