Recommandé
Recommandé
Contenu connexe
En vedette
Plus de NMJones
Plus de NMJones (17)
Geraint Lewis Ageing Well presentation
- 1. Predictive case modelling in social care and health Geraint Lewis FRCP FFPH www.nuffieldtrust.org.uk
- 2. The Nuffield Trust t: 020 7631 8450 e: info@nuffieldtrust.org.uk www.nuffieldtrust.org.uk
- 3. Case Finding • NHS predictive models • Models for social care Evaluation Remuneration
- 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 California's 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. Frequently-admitted patients 50 Average 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. Regression to the mean Average 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. Emerging Risk 50 45 Average 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. 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 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. Scotland Wales • SPARRA • PRISM model • SPARRA-MD • Welsh Predictive Risk Service
- 11. Name, Address, DOB 131178 J7KA42 Encrypted, linked data Inpatient Inpatient Name, Address, DOB 131178 J7KA42 Outpatient Outpatient J7KA42 A&E A&E Name, Address, DOB 131178 J7KA42 GP GP Name, Address, DOB 131178 J7KA42 J7KA42 76.4 131178 76.4 Decrypted data with risk score attached
- 12. 10 Million Patient-Years 10 Million Patient-Years of Data of Data Development 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. Inpatient Inpatient Outpatient Outpatient Development A&E A&E GP Sample GP J7KA42 J7KA42 J7KA42 YH8TPP YH8TPP YH8TPP G8HE9F G8HE9F G8HE9F 3LWZ67 3LWZ67 3LWZ67 2NX632 2NX632 2NX632 LG5DSD LG5DSD LG5DSD 3V9D54R 3V9D54R 3V9D54R Year 1 Year 2 Year 3
- 14. Inpatient Inpatient Outpatient Outpatient Development A&E A&E GP Sample GP J7KA42 J7KA42 J7KA42 YH8TPP YH8TPP YH8TPP G8HE9F G8HE9F G8HE9F 3LWZ67 3LWZ67 3LWZ67 2NX632 2NX632 2NX632 LG5DSD LG5DSD LG5DSD 3V9D54R 3V9D54R 3V9D54R Year 1 Year 2 Year 3
- 15. Inpatient Inpatient Outpatient Development Outpatient A&E A&E Sample GP GP J7KA42 J7KA42 J7KA42 YH8TPP YH8TPP YH8TPP G8HE9F G8HE9F G8HE9F 3LWZ67 3LWZ67 3LWZ67 2NX632 2NX632 2NX632 LG5DSD LG5DSD LG5DSD 3V9D54R 3V9D54R 3V9D54R Year 1 Year 2 Year 3
- 16. Inpatient Inpatient Outpatient Validation Outpatient A&E A&E Sample True False Positive Negative GP GP A89KP5 A89KP5 A89KP5 833TY6 833TY6 833TY6 I9QA44 I9QA44 I9QA44 85H3D 85H3D 85H3D 6445JX 6445JX 6445JX 233UMB 233UMB 233UMB False Positive RF02UH RF02UH RF02UH True Negative Year 1 Year 2 Year 3
- 17. Inpatient Inpatient Outpatient Outpatient Using the Model A&E A&E GP GP A89KP5 A89KP5 833TY6 833TY6 I9QA44 I9QA44 85H3D 85H3D 6445JX 6445JX 233UMB 233UMB RF02UH RF02UH Last Year This Year Next Year
- 18. Distribution of Future Utilisation £4,500 Actual 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)
- 21. Tackling the Inverse Care Law
- 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. Health Needs Social Care Needs • Diagnoses • Client group • Prescriptions • Disabilities • Record of Health • Record of care Contacts history PAST Predictive Model FUTURE Health Service Use Social Care Use • GP visits • Residential care • Community care • Intensive home • Hospital care care • Direct payments
- 27. Evaluation of Preventive Care 5
- 28. Overcoming regression to the mean using a control group (1) Start of intervention
- 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. Overcoming regression to the mean using a control group (4) Start of intervention
- 32. 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
- 33. geraint.lewis@nuffieldtrust.org.uk t: 020 7631 8450 e: info@nuffieldtrust.org.uk
- 35. Trend Model Cost predicts: 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 Cost Event predicts: 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 months Examples Low-cost Patient will be patient this hospitalized year will become high- Patient will cost next year have diabetic ketoacidosis
- 37. Trend Model Cost Event Actionability predicts: 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 months Examples 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)
- 38. Trend Model Cost Event Actionability Readiness to predicts: 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 months Examples 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
- 39. Trend Model Cost Event Actionability Readiness to Receptivity predicts: 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 patient Examples 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