1. PARR case finding tool Patients at risk of re- hospitalisation

2. 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!!!!

3. 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

4. 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

5. Output…… PARR risk score% flagged patients admitted within 12 months 0 - 100.0% 11 - 2011.0% 21 - 3023.8% 31 - 4035.1% 41 - 5045.3% 51 - 6056.4% 61 - 7066.0% 71 - 8073.6% 81 - 9080.5% 91 - 10091.0%

6. 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

7. 3 models 1.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

8. 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

9. Over to you…. Experiences of using the algorithm Lessons Problems Pitfalls Advantages