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       #Post#: 227--------------------------------------------------
       How do we identify risk of emergency admissions?
       By: admini5 Date: May 13, 2015, 7:57 am
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       There are a number of ways to identify patients who may be at
       high risk of future emergency admission. They include the
       following.
       [list]
       [li]Clinical knowledge, which is the default position in the
       NHS. There is little research evidence in this area. Although
       clinicians may be able to identify those currently at high risk,
       they are less able to identify those who may be at risk in the
       future (The King’s Fund 2005).[/li]
       [li]Threshold modelling, which is rules based, and identifies
       those at high risk who meet a set of criteria. Case finding has
       usually been based on threshold modelling such as identifying
       patients with repeated emergency admissions as a marker of high
       risk of future admissions. But the utility of this approach has
       been questioned as, over four to five years, admission rates and
       bed use among high-risk patients (those over 65 with at least
       two emergency admissions in one year) fall to the mean rate for
       older people (38 per cent of admissions in index year, 10 per
       cent the following year, and 3 per cent at five years)(Roland et
       al 2005).
       Alternative threshold modelling techniques such as identifying
       patients at high risk through a questionnaire administered by a
       GP practice have also been tried. The Emergency Admission Risk
       Likelihood Index (EARLI) is an example of this (Lyon et al
       2007). It comprises a six-item questionnaire used to identify
       patients over 75 who are at high risk of admission. The tool
       correctly identified more than 50 per cent of those at high or
       very high risk of emergency admission, and more than 79 per cent
       of those who were not at risk. However, this method does not
       take account of changes in health status, unless repeated
       regularly.[/li]
       [li]Predictive modelling, in which data are entered into a
       statistical model in order to calculate the risk of future
       admission. Predictive modelling is thought to be the best
       available technique (The King’s Fund 2005).[/li]
       [/list]
       Several predictive models calculate the risk of future emergency
       admission for patients with one or more previous admissions;
       using information about the patient’s age, gender and
       socio-demographic characteristics. These include the Patients at
       Risk of Re-Hospitalisation (PARR) and Scottish Patients at Risk
       of Readmission and Admission (SPARRA) models  (see Appendix 1)
       (Billings et al 2006; NHS Scotland Information Services Division
       2006).
       Other models, including The King’s Fund’s Combined
       Predictive Model, the Predicting Emergency Admissions Over the
       Next Year (PEONY) model, and the Reduce Emergency Admissions
       Risk model (Prism), use further data from primary care records
       such as prescribing or diagnosis and medical test results (The
       King’s Fund 2006; Donnan et al 2008; Welsh Assembly
       Government Department for Health and Social Services 2007).
       Different models have focused on different population groups
       – for example, those with a prior history of emergency
       hospital admission (PARR) and those aged over 65 (SPARRA)
       – whereas the Combined, PEONY and Prism models include all
       patients registered with a GP or PCT. Testing the various models
       results in varying degrees of accuracy in predicting future
       admission (see Appendix 1). Those models that include data from
       primary care records perform around 10 per cent better than
       those that rely on secondary care data alone.
       In order to improve the performance of predictive models,
       detailed data on individual patients need to be available.
       For further information, please visit:
  HTML http://www.kingsfund.org.uk/sites/files/kf/Avoiding-Hospital-Admissions-Sarah-Purdy-December2010.pdf
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