Risk Stratification
Peter Flynn
Director of Performance &
Information
Agenda
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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
– Alcohol
– Obesity
– 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
Single Data Source
Inpatient
data
PARR
How it works
•Look back 3
years & identify
factors that
contributed to
admissions
•Regression
Analysis
•Predict Risk for
next year
Combined Predictive Model
• 2nd Generation algorithm
• Risk assesses an entire population
• Uses additional sources of data:-
Multiple Data Sources
Inpatient
data
Outpatient
data
A&E data
PARR
Combined
Model
GP
Practice
data
Social
Services
data
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 Dialog
18 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
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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_projec
ts/predicting_and_reducing_readmission_t
o_hospital/resources.html
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