PARR case finding tool

Patients at risk of re-hospitalisation

Background

Risk prediction system for use by PCTs

Identifies patients at high risk of emergency re-admission to hospital

System produced by Kings Fund, New York University and Health dialog data service

Commissioned by Essex SHA on behalf of the 28 SHAs

It’s FREE!!!!

Background to project

Phase 1 – Literature review: June 2005

Phase 2 – Development of an algorithm that uses HES data to predict future risks: July 2005

Phase 3 – Development of an algorithm that links HES with other routine data on utilisation of care, in order to predict risks: January 2006

The PARR case finding algorithm

Uses hospital admission data to identify patients at high risk of re-hospitalisation in the 12 months following a “reference” hospitalisation

Produces a “risk score” for probability of future admissions which draws upon broad range of information about the patient – current hospitalisation, past hospitalisation, geographic area where patient resides, hospital of current admission

Risk scores range from 1 to 100 – higher scores having a higher risk of admission in next 12 months

Output……

PARR risk score % flagged patients admitted within 12 months

0 - 10 0.0%

11 - 20 11.0%

21 - 30 23.8%

31 - 40 35.1%

41 - 50 45.3%

51 - 60 56.4%

61 - 70 66.0%

71 - 80 73.6%

81 - 90 80.5%

91 - 100 91.0%

Characteristics of patients flagged with high risk scores (over 50): Higher level of utilisation

Significantly older

86% had multiple chronic diseases

Higher levels of anaemia

Mental illness higher

Large percentage die in hospital in the 12 months after the “reference” admission

3 models1. The “real time” algorithm” uses “real time” data to

identify level of risk of re-hospitalisation for patients hospitalised for “reference” conditions before they are discharged – requires historic data on hospitalisation as well as daily downloading of data from A&E systems

2. The “monthly” algorithm is designed to be run each month and is based on historic data as well as monthly admission data from NWCS or SUS

3. The “annual” algorithm identifies patients who have been admitted within the year and who are at risk of a subsequent admission in the next 12 months – uses historic NWCS data

Next step – implementing effective interventions

Flexible and match particular needs of each patient

Non-intrusive

Cost-effective

Co-ordinates medical care, social care and community resources

Over to you….

Experiences of using the algorithm

Lessons

Problems

Pitfalls

Advantages