risk stratification peter flynn director of performance & information

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 HolmeValleys

• 72 GP Practices

• 23 Local Authority wards

• 8 of these fall into the 25% most deprived in England and Wales

Health Need AssessmentJoint Strategic Needs Assessment refreshed 09/10CLIK 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 www.kingsfund.org.uk )

• 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

• 2nd Generation algorithm

• Risk assesses an entire population

• Uses additional sources of data:-

Inpatient data

A&E data GP Practice

dataSocial

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 process Feb 2009

Successful company Bupa Health Dialog18 month contract

Contract start Sept 2009

Wave 1 delivery April 2010 29 Practices

Wave 2 delivery June 2010 68 Practices

Project review Sept 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

http://www.kingsfund.org.uk/document.rm?id=6744

King’s Fund page of Predicting and Reducing Re-admission:

http://www.kingsfund.org.uk/current_projects/predicting_and_reducing_readmission_to_hospital/resources.html

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