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Predictive Modelling for Social Care

Predictive Modelling for Social Care. Geraint Lewis Senior Fellow The Nuffield Trust. Outline. Background Information Governance Data Linkage Modelling Social Care Predicting Impactability Service Evaluation. Care Home Admissions. Undesirable Costly Recorded in routine data

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Predictive Modelling for Social Care

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  1. Predictive Modelling for Social Care Geraint Lewis Senior Fellow The Nuffield Trust

  2. Outline • Background • Information Governance • Data Linkage • Modelling Social Care • Predicting Impactability • Service Evaluation

  3. Care Home Admissions • Undesirable • Costly • Recorded in routine data • Potentially avoidable

  4. Upstream Interventions • There is robust evidence that certain preventative interventions are effective at avoiding or delaying care home admission • But they are only be cost-effective if they are offered to people truly at high risk

  5. Predictive Factors • Many factors are known to be predictive of care home admission • Several face-to-face tools have been built using these factors

  6. Factors statistically predictive of institutionalisation

  7. Predictions based on routine data • Less labour intensive so they can stratify the population systematically and repeatedly • Avoid “non-response bias” • Can identify people with lower, emerging, risk

  8. Potential Drawbacks • Important issues of confidentiality and consent to consider • Linking data sources at individual level across health and social care is problematic where there is no NHS number in social care • The tools are never 100% accurate • Data may be missing from routine databases on certain groups

  9. Outline • Background • Information Governance • Data Linkage • Modelling Social Care • Predicting Impactability • Service Evaluation

  10. Data protection Before predictive modelling can work, we need to reconcile the following:- Predictive modelling believed to be very valuable in improving patient care But at the same time we need to protect patient confidentiality and process data appropriately

  11. Pseudonymisation in practice

  12. Solution agreed … • Process to undertake the analysis will include with it an encryption programme • Programme will be run by people not directly involved in providing care and treatment – but these people will not access the identifiable data held within the data file • The output files will be sent encrypted to the practice or other clinicians already providing care and treatment to the patients concerned • The decryption keys will be held by the PCT and will be sent separately to the health professionals involved • “It is a clear principle of the Patient Advisory Group that the first point of contact with patients should be made through a clinical team known to the patient, such as their GP practice.” • Source: PIAG (2008)

  13. Outline • Background • Information Governance • Data Linkage • Modelling Social Care • Predicting Impactability • Service Evaluation

  14. Data collected • From five sites (~ PCT/LA areas in England) • Total nine organisations: 4 PCTs, 4 LAs, 1 Care trust • 1.8M population (range 100,000-700,000)

  15. Data linkage - approach First instance: NHS number (encrypted) from LA In absence of NHS number: • Central ‘batch tracing’ ?? • Shared PCT/LA databases ?? Ultimately: • construction of ‘alternative IDs’ 97% of individuals in one site (population ~400,000) were found to have unique ‘alternative ID’. Remaining 3% - attempt match by postcode

  16. Data linkage - Summary NHS number where available (encrypted) ‘Alternative ID’ (+ postcode) where not (both encrypted)

  17. Data linkage – how good? Groups of people in social care data – how many are we able to match to GP register list (of ages 55+)? Varies, but better for those with > service use

  18. Data linkage Social & Hospital care overlap Redo ! W devon – Over 55s 267300 In sus 3 yrs 203,000 = 76% In sc 41,700 = 16% Overlap 39,300 = 94% of sc Population of over 55s registered in one PCT 90% of those with a social care contact have also had secondary care contact(s) in three years

  19. Data linkage Health and social care event timeline

  20. Outline • Background • Information Governance • Data Linkage • Modelling Social Care • Predicting Impactability • Service Evaluation

  21. Modelling results Predicting for over 75s • admission to care home / intensive home care • marked increase in social care costs (+£5,000) * stable model not found

  22. Changing the Dependent Variable Predicting for over 75s • admission to care home / intensive home care • some increase in social care costs

  23. Important model variables? Note the importance of prior social care variables

  24. Impact of adding new datasets

  25. Developing Business Cases

  26. Outline • Background • Information Governance • Data Linkage • Modelling Social Care • Predicting Impactability • Service Evaluation

  27. Outline • Background • Information Governance • Data Linkage • Modelling Social Care • Predicting Impactability • Service Evaluation

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