Patients at risk of re hospitalisation parr tool

This final result can be converted to a percentage using:. A fictional year-old woman from a relatively deprived part of London is about to be discharged from a large teaching hospital in London. Her home post code is E1 5AA. She received an emergency admission linked to her chronic obstructive pulmonary disease 7 days ago. Though she has not been in hospital within the last month, she did have two discharges following emergency admissions in the previous year.

The patient also has a history of congestive heart failure and peripheral vascular disease. This places it in our 25 to 40 deprivation band, for which the coefficient is 0. Confidence intervals and estimated costs of readmission are applied from look-up tables based on risk score. These can be found in table two of the paper published in BMJ Open. Nuffield Trust, 23 Oct Many of the studies looked at were small and not well designed. Five of 16 randomised controlled trials documented statistically significant reductions in the absolute risk of re-admission, but no single intervention or bundle of strategies were found to be consistently successful in reducing risk.

The data on costs developed here also suggest additional caution. At a risk score cut-off of 0. While improved discharge planning, arranging postdischarge follow-up visits and telephone reminders may be relatively inexpensive, other interventions such as nurse coaching and home visits can become quite costly.

These data would permit targeting of interventions, with more costly strategies limited to the patients at highest risk, but the level of available resource will undoubtedly be strained if breakeven is expected. As hospitals in England begin responding to the new financial incentives included in the — operating framework, it will be important to gather evidence about what interventions are effective and for which patients and at what cost. Areas for future research may include determining whether and how the effectiveness of interventions differs according to the underlying level of risk.

For example, it may be that patients at lower or moderate risk of re-admission have conditions or circumstances where an intervention is more likely to succeed than for patients at high risk. Equally, there may be certain sub-groups of patients within a particular risk band who are more or less amenable to preventive care. The use of predictive models as case finding tools to target preventive interventions has gained considerable currency in community-based settings.

We believe that it is important to consider how such tools might be used in the much more immediate care environment of the hospital to improve the long-term management of patients. This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author s and has not been edited for content.

Contributors The preparation of data sets and input variables and costs were undertaken by TG and IB; J B did the central modelling and reporting while AS undertook work on bootstrapping and testing derived models.

GL wrote the first draft of the paper and coordinated advice from local sites. MB advised on the analysis and results and managed the work of the research team at the Nuffield Trust.

All authors contributed to the writing of the paper. JB is the guarantor. Funding and disclaimer This research was funded by the Nuffield Trust. The study sponsor was the Chairman of the Nuffield Trust.

The sponsor had no role in and the collection, analysis and interpretation of data, in the writing of the article nor in the decision to submit it for publication.

Competing interests All authors have completed the Unified Competing Interest form at www. Ethical approval This study only involved the analysis of pseudonymous secondary data. Since there were no identifiable human subjects, ethics approval was not required for this research and informed consent was not sought. Provenance and peer review Not commissioned; externally peer reviewed. Data sharing statement Details of the derived models variables and definition are available from the authors at the nuffield trust at research nuffieldtrust.

You will be able to get a quick price and instant permission to reuse the content in many different ways. Skip to main content. Log In More Log in via Institution. Log in via OpenAthens. Log in using your username and password For personal accounts OR managers of institutional accounts.

Forgot your log in details? Register a new account? Forgot your user name or password? Search for this keyword. Advanced search. Log in via Institution. Email alerts. Article Text. Article menu. Health services research. Statistics from Altmetric. Key messages The model has been purposely designed to use only a few variables that might be entered from computerised information, or at the bedside. Strengths and limitations of this study Simples and easily implemented model. The model has low sensitivity which means high risk patients are rare.

Introduction Unplanned hospital admissions and re-admissions are regarded as markers of costly, suboptimal healthcare 1 , 2 and their avoidance is currently a priority for policy-makers in many countries. Results The derived model uses a small set of variable types included below: Patient age—used as squared value.

The number of emergency hospital discharges in the last year from any hospital. View this table: View inline View popup. Discussion We have built a predictive model using a limited set of variables that were generated from HES.

Chronic care. Cambridge : Cambridge University Press , Impact of socioeconomic status on hospital use in New York City. Health Aff ; 12 : — Health System Priorities in the Aftermath of the Crisis. Paris : OECD , Department of Health. Payment by Results Guidance for — London : Department of Health , Healthy Lives, Healthy People: transparency in outcomes, proposals for a public health outcomes framework.

Analysis of emergency day readmissions in England using routine hospital data — Is there scope for reduction? Robinson P. Purdy S. Avoiding hospital admissions: what does the research evidence say? London : King's Fund , Interventions to reduce day rehospitalization: a systematic review. Ann Intern Med ; : — 8. An evaluation of the impact of community-based interventions on hospital use. London : Nuffield Trust , Follow up of people aged 65 and over with a history of emergency admissions: analysis of routine admission data.

BMJ ; : — Predictive risk Project Literature Review. Inability of providers to predict unplanned readmissions. The last version of this model was released in December The intellectual property rights for both models is owned by the Department of Health.

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