1. Predicting risk of hospital admission and extracting GP data David Osborne Senior Public Health Information Analyst NHS Croydon

2. Overview  Risk stratification  Predictive algorithms  Extraction of GP data  Evaluation

3. Risk stratification Disease management Supported self-care Prevention and wellness promotion Very high relative risk 0.5% 10% of emergency admissions High relative risk 0.5 - 5% 25% of emergency admissions Moderate relative risk 6 - 20% 25% of emergency admissions Low relative risk 21 - 100% 40% of emergency admissions Case management Disease management Supported self-care Prevention and wellness promotion

4. Frequently-admitted patients Source: Department of Health

5. Frequently-admitted patients Source: Department of Health

6. Emerging risk Source: Department of Health

7. Techniques to find high risk patients  Clinical knowledge Referral by clinicians  Threshold modelling Set of criteria e.g. aged >65 having 2+ emergency admissions in last year  Predictive modelling Use historical data to quantify future risk of admission

8. PARR++ In-patient Records PARR++ Algorithm Patient NHS No.Risk Score XXXXX170.2% XXXXX234.9% XXXXX394.0% Linked date ??? 00101001001 10110010010 01000001010 10000010110 01001101111 10010110110

9. PARR++

10. Combined Model GP Records A&E Records Out-patient Records In-patient Records Combined Predictive Model Algorithm Patient NHS No.Risk Score XXXXX170.2% XXXXX234.9% XXXXX394.0% Linked date ??? 00101001001 10110010010 01000001010 10000010110 01001101111 10010110110

11. Combined Model

12. UK context SPARRA (www.isdscotland.org/isd/6072.html)www.isdscotland.org/isd/6072.html PRISM (www.wales.nhs.uk/IHC/page.cfm?org id=770&pid=33635)www.wales.nhs.uk/IHC/page.cfm?org id=770&pid=33635 PARR++ and Combined Model (www.kingsfund.org.uk)www.kingsfund.org.uk

13. Use of risk scores for case management: Croydon virtual wards Quarterly list of top 1000 highest risk patients Sent to virtual ward team (community matrons and ward clerks) Discussion between community matron and patient’s GP Patient admitted to virtual ward Regular contacts with community matron When risk score reduces, patient considered for discharge

14. GP data required for running combined model  List of registered patients  Read code history for last 2 years  Disease registers for long term conditions

15. Extraction methods  Manual extract using MIQUEST  Companies provide service to do regular extractions e.g. Apollo, GraphNet, HeathAnalytics  General Practice Extraction Service (GPES) being set up by NHS IC (www.ic.nhs.uk/gpes)www.ic.nhs.uk/gpes

16. Information governance  Conditions for processing ‘sensitive personal data’: ‘The processing is necessary for medical purposes, and is undertaken by a health professional or by someone who is subject to an equivalent duty of confidentiality.’ (Data Protection Act, 1998)  ‘Retraceably pseudonymised data may be considered as information on individuals which are indirectly identifiable. … In that case, although data protection rules apply, the risks at stake for the individuals with regard to the processing of such indirectly identifiable information will most often be low, so that the application of these rules will justifiably be more flexible than if information on directly identifiable individuals were processed.’ (European Data Protection Working Party, 2007)

17. Obtaining support of GPs  Get support of PEC and LMC  Show GPs the benefits and provide them with the results  More likely to respond if someone they know approaches them direct

18. Example: Croydon agreement  Specific uses agreed for aggregated data including public health purposes  Data on individual patients can be used only to identify patients for case management  Additional uses agreed with LMC on case-by-case basis

19. Case management  PSA target to reduce emergency admissions by 5% between 2004 and 2008 used as a lever for change 1  Good evidence that case management improves quality of care but majority of studies show no effect on reducing emergency admissions or bed days  Evaluation is complicated by regression to the mean 1 Predictive risk project literature review, Kings Fund, 2005

20. Regression to the mean

21. Evaluation  Simple before versus after analysis misleading Start of intervention

22. Evaluation  If tracking patients over time, compare with a control group

23. Croydon examples  Comparison with a historic control group Change (before/after) in virtual ward group Change (before/after) in control group Results of paired t-test Emergency admissions -0.048-0.042(p=0.99, 95% CI -1.41, 1.43) A&E attendances-0.765-0.180(p=0.46, 95% CI -0.96, 2.14) Average length of stay -0.570-1.003(p=0.85, 95% CI -4.96, 4.10) Emergency bed days -4.0802.740(p=0.67)

24. Croydon examples  Modelled rates by risk score by month compared with actual rates for patients on virtual wards

25. Croydon examples  Trend in average number of emergency admissions for top 100 patients in each year

26. Future developments  PARR and Combined Model to be revised  Predicting risk that is ‘impactable’  Interventions further down the risk pyramid e.g. health coaching  Other applications of risk stratification Resource allocation, performance management

27. Further information  Kings Fund www.kingsfund.org.uk  NHS Networks pages www.networks.nhs.uk/62 www.networks.nhs.uk/networks/page/1152  Nuffield Trust www.nuffieldtrust.org.uk  GPES www.ic.nhs.uk/gpes  Evidence www.bmj.com/cgi/content/full/324/7330/135 www.bmj.com/cgi/content/full/334/7583/31 David Osborne, Senior Public Health Information Analyst, NHS Croydon David.Osborne@croydonpct.nhs.uk