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PARR case finding tool

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

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PARR case finding tool

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  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……

  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 • 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 • 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 • 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

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