2. Presentation structure
Why the interest?
DH-commissioned project:
– Project details
– Learning from international evidence
– Outputs: PARR & Combined model
Risk prediction uptake & use
Future potential and plans
Further information & plans
3. Why the interest?
Context:
High emergency admissions and rising A&E
attendance
Rising numbers with LTCs
Unsustainable system: need to shift from reactive
to proactive care & treat outside hospital
Poor quality care for people with long term
conditions: little continuity of care & regular
admissions
Need to strengthen commissioning & tailor
services to the needs of population
4. Why the interest?
Other relevant issues:
Government had pledged to put in place 3000
community matrons by 2006 to manage high risk
patients
Evercare evaluation showed that the initiative
had reduced emergency admissions by 1% at
most
5. Questions arising:
1. Who are the people who will have high numbers
of unplanned admissions next year?
2. How do we identify them accurately?
3. What can we do to prevent them entering a
spiral of admissions?
7. DH-Commissioned project
Three strands:
1. Literature review: what techniques are used to
predict risk around the world?
2. Can risk of readmission be predicted using
routine inpatient data? PARR
3. Can risk of admission be predicted using linked
datasets? Combined Predictive Model
8. Findings from international literature
International literature revealed 3
main methods for “case-finding”:
1.Clinical knowledge
2.Threshold modelling
3.Predictive modelling
10. DH-Commissioned project
Three strands:
1. Literature review: what techniques are used to
predict risk around the world?
2.Can risk of readmission be predicted using
routine inpatient data? PARR
3. Can risk of admission be predicted using linked
datasets? Combined Predictive Model
11. 2) Prediction using inpatient data:
PARR
Uses just inpatient admissions data
Year of Year of
Prior utilisation admission prediction
0
Risk
score
100
Year 1 Year 2 Year 3 Year 4 Year 5
• PARR (2005)
• PARR+ (2006)
• PARR++ (2007)
12.
13.
14.
15.
16. DH-Commissioned project
Three strands:
1. Literature review: what techniques are used to
predict risk around the world?
2. Can risk of readmission be predicted using
routine inpatient data? PARR
3.Can risk of admission be predicted using
linked datasets? Combined Predictive Model
17. 3. Combined Predictive Model
PARR
High risk
Combined
Medium risk predictive
model
Low risk
18. Combined Predictive Model: data
Inpatient Outpatient A&E GP data
data data data
Social
services
data
Combined
Predictive Model
19. Risk prediction uptake
Becoming mainstream:
– Use of predictive tools is one of WCC skills
– Survey suggested:
• 80% of PCTs are using some form of
predictive tool
• 67% of PCTs are using PARR
• Very few (up to 5%) PCTs are using CPM
due to data challenges & absence of front-
end
20. Use of outputs
Various interventions being tested:
– Virtual wards
– Telephone health coaching
– Integration with social care
Other uses:
– Identifying clinical gaps for GPs to address
– Informing commissioning decisions/identifying
need
Issue: What interventions/approaches are (cost)
effective at preventing unplanned admission?
21. Future plans/potential
Update of PARR & CPM (proposal submitted to
DH)
Incorporation of more data (e.g. social care) to
predict health outcomes
Prediction of other outcomes (e.g. nursing home
admission; cost)
More refined targeting of model (e.g.
impactability)
Effective interventions
