1 SPARRA: predicting risk of emergency admission among older people Steve Kendrick Delivering for Health Information Programme ISD Scotland NHS GG&C Public Health Friday Seminar Dalian House, 1 st 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 Public health; health Improvement; health education Lower risk: supported self-care (70-80%) High risk: disease Management (15-20%) Interventions Outcomes Individuals with complex needs: case management (3-5%) Long-term conditions + interface with unscheduled care Level 1 Level 2 Level 3 Level 4 Emergency admissions Kerr Unscheduled Care Levels

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

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

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

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

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11 Some more recent trends

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17 SPARRA …….

18 SPARRA stands for… S Scottish P Patients AAtAAt R Risk of R Readmission and A Admission

19 Purpose of SPARRA Identify those people at greatest risk of emergency inpatient admissionIdentify 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 yearsCurrent 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 dataDevelop predictive model (logistic regression) based on patients for whom we do know the outcome – historic data Identify what determines the likelihood of future emergency admissionIdentify what determines the likelihood of future emergency admission Apply model to patients for whom we don’t know the outcomeApply model to patients for whom we don’t know the outcome Calculate individual risks Feed back results to front lineFeed back results to front line

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

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

23 Our approach No ‘black boxes’No ‘black boxes’ Transparent – understand what’s under the bonnetTransparent – understand what’s under the bonnet CollaborativeCollaborative EvolutionaryEvolutionary

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

25 Results: major factors emerging as predictors Number of previous emergency admissionsNumber of previous emergency admissions Time since most recent admissionTime since most recent admission AgeAge Interaction between age and previous emergency admissionsInteraction between age and previous emergency admissions DeprivationDeprivation Number of diagnosesNumber of diagnoses Most recent diagnosis – especially COPDMost recent diagnosis – especially COPD NB. NHS Board not significantNB. NHS Board not significant

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28 very high Example: individual with very high predicted probability of admission Predicted probability of admission 86%Predicted probability of admission 86% Male aged 65 to 69Male aged 65 to 69 Less than one month since most recent admissionLess than one month since most recent admission 6+ previous emergency admissions6+ previous emergency admissions Glasgow – most deprived decileGlasgow – most deprived decile Most recent admission diagnosis: COPDMost recent admission diagnosis: COPD Outcome: admitted as emergencyOutcome: admitted as emergency

29 1 st April 2006 Predictor variables Outcome year Applying the predictive model Time Period April 2003 to March 2006 April March 2007 Based on previous 3 years of hospital admissions

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

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

43 Usually 6 months until SMR01 data complete enough: how much of an issue? What might have happened in 6 monthsWhat might have happened in 6 months Patient may havePatient may have a)died – must check via local systems b)been admitted – increase in future risk c)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 patientsIdentifiable 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 levelsLocal 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“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) (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)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 peopleSPARRA 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 managementcf 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 SystemWatchMove to incorporate real-time data: via SystemWatch Incorporating primary care data.Incorporating primary care data. Needs to be led locally Needs to be led locally Relation with social care data c.f. Highland – needs to be done locally.Relation with social care data c.f. Highland – needs to be done locally. Economic aspects – what could be the pay-off?Economic aspects – what could be the pay-off? Evaluation – SPARRA to help evaluate impact of models of anticipatory careEvaluation – SPARRA to help evaluate impact of models of anticipatory care

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

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