1. Predictive case modelling in social care and health www.nuffieldtrust.org.uk Geraint Lewis FRCP FFPH

2. t : 020 7631 8450 e : info@nuffieldtrust.org.uk www.nuffieldtrust.org.uk The Nuffield Trust

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

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

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

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

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

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

12. 10 Million Patient-Years of Data 5 Million Patient-Years of Data 5 Million Patient-Years of Data Development Validation

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

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

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

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

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

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

19. NHS Combined Model

20. Clinical Profiles

21. Tackling the Inverse Care Law

22. Developing Business Cases

27. Evaluation of Preventive Care 5

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

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

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

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

33. [email_address] t: 020 7631 8450 e: [email_address]

34. Trend Model predicts: Details Examples

35. Trend Model predicts: Cost Details Model predicts which patients will become high-cost over next 6 or 12 months Examples Low-cost patient this year will become high-cost next year

36. Trend Model predicts: Cost Event Details Model predicts which patients will become high-cost over next 6 or 12 months Model predicts which patients will have an event that can be avoided Examples Low-cost patient this year will become high-cost next year Patient will be hospitalized Patient will have diabetic ketoacidosis

37. Trend Model predicts: Cost Event Actionability Details Model predicts which patients will become high-cost over next 6 or 12 months Model predicts which patients will have an event that can be avoided Model predicts which patients have features that can readily be changed Examples Low-cost patient this year will become high-cost next year Patient will be hospitalized Patient will have diabetic ketoacidosis Patient has angina but is not taking aspirin Patient does not have pancreatic cancer (Ambulatory Care Sensitive)

38. Trend Model predicts: Cost Event Actionability Readiness to engage Details Model predicts which patients will become high-cost over next 6 or 12 months Model predicts which patients will have an event that can be avoided Model predicts which patients have features that can readily be changed Model predicts which patients are most likely to engage in upstream care Examples Low-cost patient this year will become high-cost next year Patient will be hospitalized Patient will have diabetic ketoacidosis Patient has angina but is not taking aspirin Patient does not have pancreatic cancer (Ambulatory Care Sensitive) Patient does not abuse alcohol Patient has no mental illness Patient previously compliant

39. Trend Model predicts: Cost Event Actionability Readiness to engage Receptivity Details Model predicts which patients will become high-cost over next 6 or 12 months Model predicts which patients will have an event that can be avoided Model predicts which patients have features that can readily be changed Model predicts which patients are most likely to engage in upstream care Model predicts what mode and form of intervention will be most successful for each patient Examples Low-cost patient this year will become high-cost next year Patient will be hospitalized Patient will have diabetic ketoacidosis Patient has angina but is not taking aspirin Patient does not have pancreatic cancer (Ambulatory Care Sensitive) Patient does not abuse alcohol Patient has no mental illness Patient previously compliant Patient prefers email rather than telephone Patient prefers male voice rather than female Readiness to change