Predictive Modelling for Social Care

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

8. 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

9. 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

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

11. 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

12. Pseudonymisation in practice

13. 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)

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

15. 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)

16. 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

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

18. 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

19. 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

20. Data linkage Health and social care event timeline

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

22. 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

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

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

25. Impact of adding new datasets

26. Developing Business Cases

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

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