Geraint Lewis Ageing Well presentation

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Geraint Lewis Ageing Well presentation

  1. 1. Predictive case modelling in social care and health Geraint Lewis FRCP FFPH www.nuffieldtrust.org.uk
  2. 2. The Nuffield Trustt: 020 7631 8450e: info@nuffieldtrust.org.ukwww.nuffieldtrust.org.uk
  3. 3. Case Finding• NHS predictive models• Models for social careEvaluationRemuneration
  4. 4. Why Predictive Modelling? • BMJ in paper* in 2002 showed Kaiser Permanente 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 Californias Kaiser Permanente BMJ 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. 5. Frequently-admitted patients 50Average number of emergency bed days 45 40 35 30 25 20 15 10 5 0 -5 -4 -3 -2 -1 Intense +1 +2 +3 +4 year
  6. 6. Regression to the meanAverage number of emergency bed days 50 45 40 35 30 25 20 15 10 5 0 Intense -5 -4 -3 -2 -1 year +1 +2 +3 +4
  7. 7. Emerging Risk 50 45Average number of emergency bed days 40 35 30 25 20 15 10 5 0 -5 -4 -3 -2 -1 +1 +2 +3 +4 Intense year
  8. 8. Kaiser Pyramid Small numbers of people at very high riskThe Pyramidrepresents thedistribution ofrisk across thepopulation Large numbers of people at low risk [Size of shape is proportional to number of patients]
  9. 9. Patterns in routine data Inpatient Inpatient A&E data A&E data GP Practice GP Practice data data data data Outpatient Outpatient data data PARR Combined Model Census Census data data
  10. 10. Scotland Wales• SPARRA • PRISM model• SPARRA-MD • Welsh Predictive Risk Service
  11. 11. Name, Address, DOB 131178 J7KA42 Encrypted, linked data Inpatient InpatientName, Address, DOB 131178 J7KA42 Outpatient Outpatient J7KA42 A&E A&EName, Address, DOB 131178 J7KA42 GP GPName, Address, DOB 131178 J7KA42 J7KA42 76.4 131178 76.4 Decrypted data with risk score attached
  12. 12. 10 Million Patient-Years 10 Million Patient-Years of Data of DataDevelopment Validation 5 Million Patient-Years 5 Million Patient-Years 5 Million Patient-Years 5 Million Patient-Years of Data of Data of Data of Data
  13. 13. Inpatient Inpatient Outpatient Outpatient Development A&E A&E GP Sample GPJ7KA42 J7KA42 J7KA42YH8TPP YH8TPP YH8TPPG8HE9F G8HE9F G8HE9F3LWZ67 3LWZ67 3LWZ672NX632 2NX632 2NX632LG5DSD LG5DSD LG5DSD3V9D54R 3V9D54R 3V9D54R Year 1 Year 2 Year 3
  14. 14. Inpatient Inpatient Outpatient Outpatient Development A&E A&E GP Sample GPJ7KA42 J7KA42 J7KA42YH8TPP YH8TPP YH8TPPG8HE9F G8HE9F G8HE9F3LWZ67 3LWZ67 3LWZ672NX632 2NX632 2NX632LG5DSD LG5DSD LG5DSD3V9D54R 3V9D54R 3V9D54R Year 1 Year 2 Year 3
  15. 15. Inpatient Inpatient Outpatient Development Outpatient A&E A&E Sample GP GPJ7KA42 J7KA42 J7KA42YH8TPP YH8TPP YH8TPPG8HE9F G8HE9F G8HE9F3LWZ67 3LWZ67 3LWZ672NX632 2NX632 2NX632LG5DSD LG5DSD LG5DSD3V9D54R 3V9D54R 3V9D54R Year 1 Year 2 Year 3
  16. 16. Inpatient Inpatient Outpatient Validation Outpatient A&E A&E Sample True False Positive Negative GP GPA89KP5 A89KP5 A89KP5833TY6 833TY6 833TY6I9QA44 I9QA44 I9QA4485H3D 85H3D 85H3D6445JX 6445JX 6445JX233UMB 233UMB 233UMB False PositiveRF02UH RF02UH RF02UH True Negative Year 1 Year 2 Year 3
  17. 17. Inpatient Inpatient Outpatient Outpatient Using the Model A&E A&E GP GPA89KP5 A89KP5833TY6 833TY6I9QA44 I9QA4485H3D 85H3D6445JX 6445JX233UMB 233UMBRF02UH RF02UH Last Year This Year Next Year
  18. 18. Distribution of Future Utilisation £4,500Actual Average cost per patient £4,000 £3,500 £3,000 £2,500 £2,000 £1,500 £1,000 £500 £0 0 10 20 30 40 50 60 70 80 90 Predicted Risk (centile rank)
  19. 19. NHS Combined Model
  20. 20. Clinical Profiles
  21. 21. Tackling the Inverse Care Law
  22. 22. Developing Business Cases
  23. 23. How the output of predictive models are used • Case Management • Intensive Disease Management • Less Intensive Disease Management • Wellness Programmes Potential Misuses Dumping Cream-skimming Skimping
  24. 24. Health Needs Social Care Needs • Diagnoses • Client group • Prescriptions • Disabilities • Record of Health • Record of care Contacts historyPAST Predictive ModelFUTURE Health Service Use Social Care Use • GP visits • Residential care • Community care • Intensive home • Hospital care care • Direct payments
  25. 25. Evaluation of Preventive Care 5
  26. 26. Overcoming regression to themean using a control group (1) Start of intervention
  27. 27. Overcoming regression to themean using a control group (2) Start of intervention
  28. 28. Overcoming regression to themean using a control group(3) Start of intervention
  29. 29. Overcoming regression to themean using a control group (4) Start of intervention
  30. 30. Person-Based Resource Allocation• Historically, GP practice budgets set on area- based variables• New approach is person-based• Exclude certain variables to avoid perverse incentives – Procedures – Disease severity
  31. 31. geraint.lewis@nuffieldtrust.org.uk t: 020 7631 8450 e: info@nuffieldtrust.org.uk
  32. 32. Trend Modelpredicts: DetailsExamples
  33. 33. Trend Model Costpredicts: Details Model predicts which patients will become high-cost over next 6 or 12 monthsExamples Low-cost patient this year will become high- cost next year
  34. 34. Trend Model Cost Eventpredicts: Details Model predicts Model predicts which patients which patients will become will have an high-cost over event that can next 6 or 12 be avoided monthsExamples Low-cost Patient will be patient this hospitalized year will become high- Patient will cost next year have diabetic ketoacidosis
  35. 35. Trend Model Cost Event Actionabilitypredicts: Details Model predicts Model predicts Model predicts which patients which patients which patients will become will have an have features high-cost over event that can that can readily next 6 or 12 be avoided be changed monthsExamples Low-cost Patient will be Patient has patient this hospitalized angina but is year will not taking become high- Patient will aspirin cost next year have diabetic Patient does ketoacidosis not have pancreatic cancer (Ambulatory Care Sensitive)
  36. 36. Trend Model Cost Event Actionability Readiness topredicts: engage Details Model predicts Model predicts Model predicts Model predicts which patients which patients which patients which patients will become will have an have features are most likely high-cost over event that can that can readily to engage in next 6 or 12 be avoided be changed upstream care monthsExamples Low-cost Patient will be Patient has Patient does patient this hospitalized angina but is not abuse year will not taking alcohol become high- Patient will aspirin cost next year have diabetic Patient does Patient has no ketoacidosis not have mental illness pancreatic cancer (Ambulatory Patient Care Sensitive) previously compliant
  37. 37. Trend Model Cost Event Actionability Readiness to Receptivitypredicts: engage Details Model predicts Model predicts Model predicts Model predicts Model predicts which patients which patients which patients which patients what mode and will become will have an have features are most likely form of high-cost over event that can that can readily to engage in intervention next 6 or 12 be avoided be changed upstream care will be most months successful for each patientExamples Low-cost Patient will be Patient has Patient does Patient prefers patient this hospitalized angina but is not abuse email rather year will not taking alcohol than telephone become high- Patient will aspirin cost next year have diabetic Patient does Patient has no Patient prefers ketoacidosis not have mental illness male voice pancreatic rather than cancer female (Ambulatory Patient Care Sensitive) previously compliant Readiness to change

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