Risk Stratification Peter Flynn Director of Performance & Information

Agenda Kirklees What is Risk Stratification? How does it work? Business Case for NHS Kirklees Progress to date Next Steps

Key Statistics Population of over 400,000 –433,000+ by 2018 Covers urban areas of Huddersfield, Dewsbury, Batley, Cleckheaton, Liversedge, Heckmondwike and Mirfield Rural areas in Colne and Holme Valleys 72 GP Practices 23 Local Authority wards 8 of these fall into the 25% most deprived in England and Wales

Health Need Assessment Joint Strategic Needs Assessment refreshed 09/10 CLIK and Young People’s survey Issues: –Smoking– Obesity –Alcohol– Physical activity Priorities: –LTC– WOCBA –Emotional health and well-being

Risk Stratification 101 Risk Stratification is a tool to assist in the identification of people who are at most risk of (re)admission to hospital. History in the NHS –2005 DH competitive tender to identify patients at high risk of admission to hospital –Tender won by King’s Fund, New York University and Health Dialog –First tools piloted in UK in 2006/7. Algorithm made available to the NHS.

…continued Products were PARR family of tools (PARR = Patients At Risk of Readmission) (PARR PARR+ PARR++ downloadable from King’s Fund Web site ) Clue in the name –Patients –Readmission Risk assessed approx 5% of a population who had already had a recent emergency admission

Inpatient data PARR Single Data Source

Look back 3 years & identify factors that contributed to admissions Regression Analysis Predict Risk for next year How it works

Combined Predictive Model 2 nd Generation algorithm Risk assesses an entire population Uses additional sources of data:-

Inpatient data A&E dataGP Practice data Social Services data Outpatient data PARR Multiple Data Sources Combined Model

Business Case Unplanned Admissions rising Hospital re-configuration – Mid Yorks Need to reduce unplanned admissions by 16% (over 3 years) via whole system LTC service redesign.  The predictive risk tool is integral to this whole system redesign

Evidence that it works? Various studies, including Croydon –6 months data –Predicted next 6 months using PARR+ and CPM –Waited 6 months to see what actually happened –Compared Prediction vs Actual –PARR+ approx 60% accurate (and only subset of patients) –CPM approx 85% accurate (and whole population)

Chronic Care Systems Characteristics of High Performing Chronic Care Systems 1 : 1.Universal coverage 2.Care free at the point of use 3.Delivery system should focus on prevention of ill health not just treatment of sickness 4.Priority should be given to patients to self manage their conditions with support from carers and families 5.Priority is given to primary health care 6.Population management is emphasised through the use of risk stratification tools and care planning 7.Care should be integrated – primary, community, social care, secondary care 8.Exploit the potential benefits of information technology in improving chronic disease 9.Care should be properly coordinated especially in those with multiple conditions including patient activation 10.Link the above 9 characteristics into a coherent whole to achieve cumulative impact 1 Chris Ham. The ten characteristics of the high-performing chronic care system. Health Economics, Policy and Law, 2010; 5(01); 1-20

Process so Far Tendering processFeb 2009 Successful companyBupa Health Dialog 18 month contract Contract startSept 2009 Wave 1 deliveryApril Practices Wave 2 deliveryJune Practices Project reviewSept 2010

What have we bought? Access to IP to make Risk modelling work Resources to deliver the project Use of extract routines –HES/SUS –Apollo/MIQUEST Integrated Care Manager (ICM) –By Practice –PCT –Refreshed quarterly then monthly to Mar 2011

Critical Success Factors Formal Project led by LTC Programme Board. N.B. not an IM&T project Clinical Engagement (68/72 practices) & Clinical Leadership Dedicated PCT project management and practice support (2 wte) Data sharing agreement inc. Pseudonymisation GP Incentive Scheme Consent Training – GP and Practice Manager Validate 0-0.5% of practice population. (Should be known) 7 x monthly MDT meetings focused on 0.5-5% of practice population

Challenges Clinical Engagement – Key Lead (left area) Significant time investment with GPs and Practices to get involved Technical issues with MIQUEST queries & TPP Time consuming for Data Quality Team – dedicated resource 1 WTE – extraction & loading of software. Needed more Embedding use of the tool into normal practice

Technical processes Pseudonymisation Software HES/SUS extracts for Acute data Apollo remote extraction for EMIS LV and Synergy practices MIQUEST for TPP and EMIS PCS practices Microsoft Access “front end” Microsoft Excel output (Practice ICM)

What next… ICM delivered monthly September 2010 to March 2011 Training offered to Practices until August 2010 Development of focus groups Clinical Engagement Group Benefits Realisation Group Infrastructure Group Ongoing support (“drop in”, newsletter etc..)

Next Steps: Critical Success Factors Using the information effectively, i.e. clinical engagement and MDT meetings “Population management is emphasised through the use of risk stratification tools and care planning” We believe that we will succeed!

Summary Risk Stratification tools are shown to work Combined Predictive Model the most advanced Significant savings and quality improvements around LTC need to made in Kirklees Our future Acute activity is predicated on achieving these savings Excellent progress so far – Clinical engagement

More Info King’s Fund Report on Combined Predictive Model =6744 King’s Fund page of Predicting and Reducing Re-admission: ts/predicting_and_reducing_readmission_t o_hospital/resources.html

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