Predictive Models and data linkage

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Predictive Models and data linkage

  1. 1. Predictive Models and Data LinkageSharing international experience: Linking disease registryinformation and predictive modelling to improve quality andefficiencySeptember 2012Martin BardsleyHead of ResearchThe Nuffield Trust © Nuffield Trust
  2. 2. Applications of predictive risk in the UK• Case finding for people at high risk of admission seen as increasingly important for people with LTCs and complex conditions• Examples of predicting across health and social care• Scope to make the most of linked data sets in describing care pathways• Evaluation and risk adjustment © Nuffield Trust
  3. 3. Predictive risk and case finding © Nuffield Trust
  4. 4. Predictive modelling in UK • BMJ in paper* in 2002 showed Kaiser Permanente in California seemed to provide higher-quality healthcare than the NHS at a lower cost. Kaiser identify high risk people in their population and manage them intensively to avoid admissions • • Modelling aims to identify people at risk of high costs in future • Relies on exploiting existing information +ve: systematic; not costly data collections; fit into existing systems -ve: information collected may not be predictive • *Getting more for their dollar: a comparison of the NHS with Californias Kaiser Permanente BMJ 2002;324:135- 143 © Nuffield Trust
  5. 5. Predictive Models Identify who will bewhere on next year’s Kaiser Pyramid © Nuffield Trust
  6. 6. Regression to the mean:Change in average number of emergency bed days Predictive models try to identify people here © Nuffield Trust
  7. 7. Extending models beyond healthcare © Nuffield Trust
  8. 8. Information flows © Nuffield Trust
  9. 9. Protecting individuals identities © Nuffield Trust
  10. 10. Looking at and individuals history of careOne person’s story © Nuffield Trust
  11. 11. Typical accuracy models currently used topredict hospital admission Model Risk threshold PPV (%) Sensitivity (%) PARR (England) 50 65.3 54.3 70 77.4 17.8 80 84.3 8.1 SPARRA (Scotland) 50 59.4 18.0 70 76.1 3.3 S Care model 50 55 19 (Pooled £1K) 70 60 10 © Nuffield Trust
  12. 12. Range of case finding models available SPARRA PARR (++) SPARRA MD Combined Predictive Model PRISM PEONY AHI Risk adjuster LACE ACGs (John Hopkins) MARA (Milliman Advanced Risk Adjuster) DxCGs (Verisk) Dr Foster Intelligence SCOPE RISC (United Health Group) Variants on basic admission/readmission predictions: Short term readmissions Social care costs Condition specific tools © Nuffield Trust
  13. 13. Wider applications of linked data © Nuffield Trust
  14. 14. Using the data available © Nuffield Trust
  15. 15. Testing for gaps in care © Nuffield Trust
  16. 16. North West e-lab © Nuffield Trust
  17. 17. Relative size of data sets collected For one WSD areaAccident and Outpatients Inpatients Social care Community GPsemergency 1,680,000 records 360,000 records 240,000 records matrons 60 practices350,000 records 20,000 records 48.5 million records March 2011 © Nuffield Trust
  18. 18. Data linkageSocial & secondary care interface © Nuffield Trust
  19. 19. Inpatient and Social Care costs per person in final yearof life by age bandover two lines £12,000 £10,000 £8,000 £6,000 Social care £4,000 Hospital IP care £2,000 SC+ Hosp £0 40 50 60 70 80 90 100 Age Band © Nuffield Trust
  20. 20. Number of inpatient admissions (with 95% confidence intervals) per person by age according to type of social care receivedBardsley M, Georghiou T, Chassin L, Lewis G, Steventon A, and Dixon J. Overlap of hospital use and social care in older people in England J Health © Nuffield TrustServ Res Policy jhsrp.2011.010171; published ahead of print 23 February 2012,
  21. 21. Describing patterns of social care around cancer diagnosis.Linkage to cancer registry © Nuffield Trust
  22. 22. What was the average cost of hospital care? © Nuffield Trust
  23. 23. GP visits around cancer diagnosis © Nuffield Trust
  24. 24. Risk adjustment and Evaluationa. Prospective Trialsb. Retrospective evaluations © Nuffield Trust
  25. 25. Using risk scores within a randomised trialEnsuring even mix of patients Analysis by risk subgroupMarch 2011 © Nuffield Trust
  26. 26. Information flows for this analysisLocal Secondary Uses Service Encrypted client-event Link to create Combined Nuffieldoperational based Model Trustsystems GP Encrypted client-event based Linked Hospital Encrypted datasets Episodes client-event Statistics based HES-ONS Encrypted mortality data client-event based Community Encrypted systems client-event based Social care Encrypted client-event based © Nuffield Trust
  27. 27. Distribution of Combined Model risk scoresImportance of risk adjustment Very high risk Top 0.5% Top 10% High risk Moderate risk 0.5% - 5% Low risk 5% - 20% 10% - 45% 20% - 100% 45% - 85% 85% - 100% General population WSD participants © Nuffield Trust
  28. 28. Exploiting admin data within an RCT- trends inemergency hospital admissions Start of trial Able to chart hospital use before recruitment © Nuffield Trust
  29. 29. Linked data in RCTS• Enables larger sample sizes as its relatively cheap information• Able to generate multiple outcome measures• Track patient histories before baseline – and inform risk adjustment• Generate intermediate points• BUT• Constrained by type of information collected and quality• May exclude care from some sectors © Nuffield Trust
  30. 30. Retrospective evaluationsThe Partnership for Older People Projects (POPPs)•£60m investment by DH with aim to: “shift resources and culture away from institutional and hospital- based crisis care”•146 interventions piloted in 29 sites.•National evaluation of whole programmefound £1.20 saving in bed days per £1 “We recommend expanding thespent. Partnerships for Older People Projects (POPPs) approach to prevention across all local authorities and PCTs.” © Nuffield Trust
  31. 31. From the 146 interventions offered under POPP, weselected 8 for an in-depth study of hospital use Support workers for community matrons Intermediate care service with generic workers Integrated health and social care teams Out-of-hours and daytime response service + 4 different short term assessment and signposting services © Nuffield Trust
  32. 32. Our preferred option for this evaluation:link participants to HES through a trusted third party Collate files and add NHSParticipating sites numbers Information Collate patient lists Centre Derive HES ID Nuffield Trust Patient identifiers Trial information (e.g. Non-patient identifiable keys (e.g. NHS number) start and end date) (e.g. HES ID, pseudonymised © Nuffield Trust NHS number)March 2011
  33. 33. Prevalence of health diagnoses categories in interventionand control groups © Nuffield Trust
  34. 34. Overcoming regression to the mean using a controlgroup Intervention 0.3 Start of intervention Number of emergency hospital admissions per head per month 0.2 0.1 0.0 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 MonthMarch 2011 © Nuffield Trust
  35. 35. Overcoming regression to the mean using a controlgroup Intervention 0.3 Start of intervention Number of emergency hospital admissions per head per month 0.2 0.1 0.0 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 MonthMarch 2011 © Nuffield Trust
  36. 36. Overcoming regression to the mean using a controlgroup Intervention 0.3 Start of intervention Number of emergency hospital admissions per head per month 0.2 0.1 0.0 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 MonthMarch 2011 © Nuffield Trust
  37. 37. Overcoming regression to the mean using a controlgroup Control Intervention 0.3 Start of intervention Number of emergency hospital admissions per head per month 0.2 0.1 0.0 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 MonthMarch 2011 © Nuffield Trust
  38. 38. Impact of eight different interventions on hospital use © Nuffield Trust
  39. 39. Summary• Predictive modelling practical case finding tool for identifying high risk patients• Possible to screen large populations using existing data• Scope to extend linkage over time and across data sets to give a broader view of patients’ journey• Large data sets can be used in both prospective studies (RCTs) and enable retrospective analyses using matched controls• Biggest weakness with existing administrative data is the limited level of clinical information – yet greater use of clinical records, audits and registries is possible © Nuffield Trust
  40. 40. www.nuffieldtrust.org.ukSign-up for our newsletterwww.nuffieldtrust.org.uk/newsletterFollow us on Twitter(http://twitter.com/NuffieldTrust) © Nuffield Trust © Nuffield Trust

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