Risk Prediction in England

Natasha Curry, 14 January 2010

Presentation structure

Why the interest?DH-commissioned project:– Project details– Learning from international evidence– Outputs: PARR & Combined model

Risk prediction uptake & useFuture potential and plansFurther information & plans

Why the interest?

Context:High emergency admissions and rising A&E attendanceRising numbers with LTCsUnsustainable system: need to shift from reactive to proactive care & treat outside hospitalPoor quality care for people with long term conditions: little continuity of care & regular admissionsNeed to strengthen commissioning & tailor services to the needs of population

Why the interest?

Other relevant issues:Government had pledged to put in place 3000 community matrons by 2006 to manage high risk patientsEvercare evaluation showed that the initiative had reduced emergency admissions by 1% at most

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?

DH-Commissioned project

> Research team:> The King’s Fund> New York University> Health Dialog

Timescale:> March 2005 – Dec 2005…

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

Findings from international literature

International literature revealed 3 main methods for “case-finding”:

1.Clinical knowledge2.Threshold modelling3.Predictive modelling

Non-predictive v. predictive methods

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

2) Prediction using inpatient data: PARR

Uses just inpatient admissions data

• PARR (2005)• PARR+ (2006)• PARR++ (2007)

Year 1 Year 2 Year 3 Year 4 Year 5

Year of admission

Year of predictionPrior utilisation

Risk score

0

100

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

3. Combined Predictive Model

High risk

Medium risk

Low risk

PARR

Combined predictive model

Combined Predictive Model: data

Inpatient data

Outpatient data

A&E data

GP data

Social services data Combined

Predictive Model

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

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

needIssue: What interventions/approaches are (cost) effective at preventing unplanned admission?

Future plans/potential

Update of PARR & CPM (proposal submitted to DH)Incorporation of more data (e.g. social care) to predict health outcomesPrediction of other outcomes (e.g. nursing home admission; cost)More refined targeting of model (e.g. impactability)Effective interventions

Further information

Details of the models & work on KF website: – www.kingsfund.org.uk