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Predictive Modelling and its Benefits

Predictive Modelling and its Benefits. Dr. Geraint Lewis Senior Fellow The Nuffield Trust. The Nuffield Trust. Charitable Organization founded in 1940 Formerly a grant-giving organization Since 2008 we have been conducting in-house research and policy analysis

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Predictive Modelling and its Benefits

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  1. Predictive Modelling and its Benefits Dr. Geraint Lewis Senior Fellow The Nuffield Trust

  2. The Nuffield Trust • Charitable Organization founded in 1940 • Formerly a grant-giving organization • Since 2008 we have been conducting in-house research and policy analysis • Promote independent analysis and informed debate on healthcare policy across the UK

  3. Outline • Rationale • Building a Predictive Model • Predictive Modelling in the UK • Other Applications of Risk Adjustment • Impactability Models

  4. Why Predictive Modelling? • BMJ in paper* in 2002 showed KaiserPermanente in California seemed to provide higher quality healthcare than the NHS at a lower cost *Getting more for their dollar: a comparison of the NHS with California's Kaiser PermanenteBMJ 2002;324:135-143 • Kaiser identify high risk people in their population and manage them intensively to avoid admissions • Inaccurate Approaches: • Clinician referrals • Threshold approach (e.g. all patients aged >65 with 2+ admissions)

  5. 50 45 40 35 30 Average number of emergency bed days 25 20 15 10 5 0 - 1 + 1 + 2 + 3 + 4 - 5 - 4 - 3 - 2 Intense year Frequently-admitted patients

  6. 50 45 40 35 30 Average number of emergency bed days 25 20 15 10 5 0 Intense year - 5 - 4 - 3 - 2 - 1 + 1 + 2 + 3 + 4 Regression to the mean

  7. 50 45 40 35 30 25 Average number of emergency bed days 20 15 10 5 0 + 1 + 2 + 3 + 4 - 1 - 5 - 4 - 3 - 2 Intense year Emerging Risk

  8. Kaiser Pyramid Small numbers of people at very high risk The Pyramid represents the distribution of risk across the population Large numbers of people at low risk [Size of shape is proportional to number of patients]

  9. Patterns in routine data A&E data GP Practice data Inpatient data Outpatient data PARR Combined Model Census data

  10. Name, Address, DOB 131178 131178 131178 J7KA42 131178 J7KA42 J7KA42 J7KA42 J7KA42 J7KA42 76.4 76.4 Name, Address, DOB Name, Address, DOB Name, Address, DOB 131178 Encrypted, linked data  Inpatient  Outpatient  A&E  GP Decrypted data with risk score attached

  11. Randomised 5 Million Patient-Years of Data 5 Million Patient-Years of Data Development Validation Predictive Model 10 Million Patient-Years of Data

  12. J7KA42 3LWZ67 YH8TPP G8HE9F 3LWZ67 2NX632 LG5DSD 3V9D54R J7KA42 G8HE9F YH8TPP 2NX632 3V9D54R J7KA42 YH8TPP G8HE9F 3LWZ67 2NX632 LG5DSD 3V9D54R LG5DSD Year 1 Year 2 Year 3  Inpatient  Outpatient  A&E  GP Development Sample

  13. J7KA42 3LWZ67 YH8TPP G8HE9F 3LWZ67 2NX632 LG5DSD 3V9D54R J7KA42 G8HE9F YH8TPP 2NX632 3V9D54R J7KA42 YH8TPP G8HE9F 3LWZ67 2NX632 LG5DSD 3V9D54R LG5DSD Year 1 Year 2 Year 3  Inpatient  Outpatient  A&E  GP Development Sample

  14. J7KA42 3LWZ67 YH8TPP G8HE9F 3LWZ67 2NX632 LG5DSD 3V9D54R J7KA42 G8HE9F YH8TPP 2NX632 3V9D54R J7KA42 YH8TPP G8HE9F 3LWZ67 2NX632 LG5DSD 3V9D54R LG5DSD Year 1 Year 2 Year 3  Inpatient  Outpatient  A&E  GP Development Sample

  15. True Positive 233UMB 833TY6 A89KP5 RF02UH I9QA44 6445JX 85H3D 833TY6 I9QA44 A89KP5 85H3D 233UMB A89KP5 6445JX 233UMB 6445JX RF02UH I9QA44 833TY6 RF02UH 85H3D False Negative False Positive True Negative Year 1 Year 2 Year 3 Validation Sample  Inpatient  Outpatient  A&E  GP

  16. A89KP5 RF02UH 833TY6 I9QA44 85H3D 233UMB I9QA44 6445JX 833TY6 85H3D 6445JX 233UMB RF02UH A89KP5 Last Year This Year Next Year  Inpatient  Outpatient  A&E  GP Using the Model

  17. £4,500 £4,000 £3,500 £3,000 Average cost per patient £2,500 £2,000 £1,500 £1,000 Actual £500 £0 0 10 20 30 40 50 60 70 80 90 Predicted Risk (centile rank) Distribution of Future Utilisation is Exponential

  18. SPARRA Models being planned PARR & Combined Model PRISM

  19. Scotland Wales PRISM model Welsh Predictive Risk Service • SPARRA • SPARRA-MD

  20. Current work on a person-based resource allocation(PBRA) formula for England will potentially open up a much wider interest in these types of tools. • PBRA predicts per capita inpatient and outpatient costs of individuals over a future year and groups these costs at GP practice level

  21. Comparative performance requires standardisation • This is to ensure that any observed differences in performance are not due to factors beyond the control of the organisation under scrutiny

  22. Nuffield Trust is evaluatingcomplex preventive interventions including: • Telehealth and Telecare devices (WSD) • Partnerships for Older People Pilots (POPPs) • Virtual Wards (VWs) • Integrated care pilots (ICPs)

  23. 5 Evaluation of Integrated Care

  24. Overcoming regression to the mean using a control group (1) Start of intervention

  25. Overcoming regression to the mean using a control group (2) Start of intervention

  26. Overcoming regression to the mean using a control group (3) Start of intervention

  27. Overcoming regression to the mean using a control group (4) Start of intervention

  28. Future Agenda • Ageing population and increasing prevalence of chronic diseases • Predictive Modelling is being facilitated by • Increasing availability of large data sets • Ability to link data at individual level pseudonymously • Improved computing power • Challenge for predictive modelling now will be in increasing the impact of preventive care on health outcomes and potential cost savings.

  29. Trend

  30. Trend

  31. Trend

  32. Trend

  33. Trend

  34. Trend

  35. Potential Misuses • Dumping • Cream-skimming • Skimping How the output of predictive models are used • Case Management • Intensive Disease Management • Less Intensive Disease Management • Wellness Programs

  36. geraint.lewis@nuffieldtrust.org.ukwww.nuffieldtrust.org.uk

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