SPARRA: predicting risk of emergency admission among older people

1. SPARRA: predicting risk of emergency admission among older people Steve Kendrick Delivering for Health Information Programme ISD Scotland www.isd.scotland.org/dhip NHS GG&C Public Health Friday Seminar Dalian House, 1st December 2006

2. Providing information to support ‘Kerr’ and “Delivering for Health” is a key priority for ISD Scotland. The Delivering for Health Information Programme supports a specific focus of “Delivering for Health”.

3. Long-term conditions + interface with unscheduled care Level 4 Level 3 Individuals with complex needs: case management (3-5%) Emergency admissions Level 2 High risk: disease Management (15-20%) Level 1 Kerr Unscheduled Care Levels Lower risk: supported self-care (70-80%) Interventions Outcomes Public health; health Improvement; health education

4. Long-term conditions + interface with unscheduled care Level 4 DfHIP Level 3 Individuals with complex needs: case management (3-5%) Emergency admissions Level 2 High risk: disease Management (15-20%) Level 1 Kerr Unscheduled Care Levels Lower risk: supported self-care (70-80%) Interventions Outcomes Public health; health Improvement; health education

5. DfHIP Priorities around ‘the top of pyramid’ Economics: yield curves SPARRA High risk patients ? VHIUs Very high intensity users Care homes End of life costs End of life care

6. Economics: yield curves SPARRA High risk patients Primary care SPARRA ? Top 5% bed days Care homes Emergency admissions: comparative trends End of life costs End of life care GP emergency admission rates Information for CHPs LTCs/risk stratification

7. Some old friends … what the world looked like before Kerr

11. Some more recent trends

17. SPARRA …….

18. SPARRA stands for… Scottish Patients At Risk of Readmission and Admission

19. Purpose of SPARRA • Identify those people at greatest risk of emergency inpatient admission • Current cohort: people aged 65 and over with at least one emergency admission in the previous three years

20. Steps in implementing model • Develop predictive model (logistic regression) based on patients for whom we do know the outcome – historic data • Identify what determines the likelihood of future emergency admission • Apply model to patients for whom we don’t know the outcome • Calculate individual risks • Feed back results to front line

21. Developing the predictive model Cohort includes all aged 65+ with an emergency admission in previous three years (around 25% of 65+ pop.) 1st January 2004 Time Period 2002 2004 2003 2001 Outcome year Predictor variables

22. The shoulders upon which we stand • Substantial American literature see e.g. King’s Fund literature review • King’s Fund: John Billings • NHS Tayside/University of Dundee model – Peter Donnan • Highland; Lanarkshire; Ayrshire and Arran

23. Our approach • No ‘black boxes’ • Transparent – understand what’s under the bonnet • Collaborative • Evolutionary

24. Independent variables • Number of previous emergency, elective, day case admissions; total bed days • Time since most recent emergency admission • Age/gender • Deprivation • Most recent admission diagnosis, number of different diagnosis groups. • NHS Board

25. Results: major factors emerging as predictors • Number of previous emergency admissions • Time since most recent admission • Age • Interaction between age and previous emergency admissions • Deprivation • Number of diagnoses • Most recent diagnosis – especially COPD • NB. NHS Board not significant

28. Example: individual with very high predicted probability of admission • Predicted probability of admission 86% • Male aged 65 to 69 • Less than one month since most recent admission • 6+ previous emergency admissions • Glasgow – most deprived decile • Most recent admission diagnosis: COPD • Outcome: admitted as emergency

29. Applying the predictive model Based on previous 3 years of hospital admissions 1st April 2006 Time Period to April 2006- March 2007 March 2006 April2003 Outcome year Predictor variables

41. How well does the model perform • Reasonable area under the ROC. 0.69 compared with c0.8 when e.g. primary care variables included (c.f 0.685 King’s Fund hospital-based model) • Likely to be identifying the great bulk of the high risk patients out there in the community c 75-90%

42. Applying the predictive model Now 1st April 2006 Time Period April 2006 to March 2007 March 2006 April 2003 to Outcome year Predictor variables

43. Usually 6 months until SMR01 data complete enough: how much of an issue? • What might have happened in 6 months • Patient may have • died – must check via local systems • been admitted – increase in future risk • not been admitted – decline in future risk It is an issue, not a showstopper – but not satisfactory

44. Forms of feedback • Identifiable details of high-risk patients • fed back on CD on receipt of confidentiality form • values of model variables as well as ID and probabilities • Local distributions of risk levels • how many people at all levels of risk • By Board, CHP, practice

45. The role of SPARRA? Original conception – fairly narrow, mechanical SPARRA identifies a pool of high-risk patients Further local assessment identifies those for whom e.g. case management is appropriate Full stop

46. Emerging functions: SPARRA as a focus for integration • “international research suggests that integration is most needed and works best when it focuses on a specifiable group of people with complex needs, and where the system is clear and readily understood by service users (and preferably designed with them as full partners)” Integrated Care: A Guide, Integrated Care Network (cited by David Colin-Thome)

47. Emerging functions: SPARRA as a seed • Local teams often use SPARRA in combination with other sources of local information (e.g. GP registers) • SPARRA may become just one component of a dynamic, multi-source locally owned register of vulnerable people • cf Exeter. Wide range of sources for up-to-date list which ‘keeps tabs on’ vulnerable people. No high tech/IT. Based on commitment and case management

48. Further development of model • Move to incorporate real-time data: via SystemWatch • Incorporating primary care data. Needs to be led locally • Relation with social care data c.f. Highland – needs to be done locally. • Economic aspects – what could be the pay-off? • Evaluation – SPARRA to help evaluate impact of models of anticipatory care

49. Current take up of SPARRA • Around 4 Boards motoring • 6-10 Boards/CHPs – very keen – have received data (i.e. around half of CHPs have data either directly or indirectly) • Most of rest – in discussion • A very few – still to start a conversation

50. The response to SPARRA output • Starting to get feedback: the results seem to be making reasonable sense • Major frustration: based on out-of-date data • This is primary use of healthcare information: helping determine how to deliver the best care to real people • Only the beginning