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      Evaluation of a predevelopment service delivery intervention: an application to improve clinical handovers

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          Abstract

          Background

          We developed a method to estimate the expected cost-effectiveness of a service intervention at the design stage and ‘road-tested’ the method on an intervention to improve patient handover of care between hospital and community.

          Method

          The development of a nine-step evaluation framework:

          1. Identification of multiple endpoints and arranging them into manageable groups;

          2. Estimation of baseline overall and preventable risk;

          3. Bayesian elicitation of expected effectiveness of the planned intervention;

          4. Assigning utilities to groups of endpoints;

          5. Costing the intervention;

          6. Estimating health service costs associated with preventable adverse events;

          7. Calculating health benefits;

          8. Cost-effectiveness calculation;

          9. Sensitivity and headroom analysis.

          Results

              Literature review suggested that adverse events follow 19% of patient discharges, and that one-third are preventable by improved handover (ie, 6.3% of all discharges). The intervention to improve handover would reduce the incidence of adverse events by 21% (ie, from 6.3% to 4.7%) according to the elicitation exercise. Potentially preventable adverse events were classified by severity and duration. Utilities were assigned to each category of adverse event. The costs associated with each category of event were obtained from the literature. The unit cost of the intervention was €16.6, which would yield a Quality Adjusted Life Year (QALY) gain per discharge of 0.010. The resulting cost saving was €14.3 per discharge. The intervention is cost-effective at approximately €214 per QALY under the base case, and remains cost-effective while the effectiveness is greater than 1.6%.

          Conclusions

          We offer a usable framework to assist in ex ante health economic evaluations of health service interventions.

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          Most cited references19

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          Measuring potentially avoidable hospital readmissions.

          The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals.
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            Interventions aimed at reducing problems in adult patients discharged from hospital to home: a systematic meta-review

            Background Many patients encounter a variety of problems after discharge from hospital and many discharge (planning and support) interventions have been developed and studied. These primary studies have already been synthesized in several literature reviews with conflicting conclusions. We therefore set out a systematic review of the reviews examining discharge interventions. The objective was to synthesize the evidence presented in literature on the effectiveness of interventions aimed to reduce post-discharge problems in adults discharged home from an acute general care hospital. Methods A comprehensive search of seventeen literature databases and twenty-five websites was performed for the period 1994–2004 to find relevant reviews. A three-stage inclusion process consisting of initial sifting, checking full-text papers on inclusion criteria, and methodological assessment, was performed independently by two reviewers. Data on effects were synthesized by use of narrative and tabular methods. Results Fifteen systematic reviews met our inclusion criteria. All reviews had to deal with considerable heterogeneity in interventions, populations and outcomes, making synthesizing and pooling difficult. Although a statistical significant effect was occasionally found, most review authors reached no firm conclusions that the discharge interventions they studied were effective. We found limited evidence that some interventions may improve knowledge of patients, may help in keeping patients at home or may reduce readmissions to hospital. Interventions that combine discharge planning and discharge support tend to lead to the greatest effects. There is little evidence that discharge interventions have an impact on length of stay, discharge destination or dependency at discharge. We found no evidence that discharge interventions have a positive impact on the physical status of patients after discharge, on health care use after discharge, or on costs. Conclusion Based on fifteen high quality systematic reviews, there is some evidence that some interventions may have a positive impact, particularly those with educational components and those that combine pre-discharge and post-discharge interventions. However, on the whole there is only limited summarized evidence that discharge planning and discharge support interventions have a positive impact on patient status at hospital discharge, on patient functioning after discharge, on health care use after discharge, or on costs.
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              Methods to elicit beliefs for Bayesian priors: a systematic review.

              Bayesian analysis can incorporate clinicians' beliefs about treatment effectiveness into models that estimate treatment effects. Many elicitation methods are available, but it is unclear if any confer advantages based on principles of measurement science. We review belief-elicitation methods for Bayesian analysis and determine if any of them had an incremental value over the others based on its validity, reliability, and responsiveness. A systematic review was performed. MEDLINE, EMBASE, CINAHL, Health and Psychosocial Instruments, Current Index to Statistics, MathSciNet, and Zentralblatt Math were searched using the terms (prior OR prior probability distribution) AND (beliefs OR elicitation) AND (Bayes OR Bayesian). Studies were evaluated on: design, question stem, response options, analysis, consideration of validity, reliability, and responsiveness. We identified 33 studies describing methods for elicitation in a Bayesian context. Elicitation occurred in cross-sectional studies (n=30, 89%), to derive point estimates with individual-level variation (n=19; 58%). Although 64% (n=21) considered validity, 24% (n=8) reliability, 12% (n=4) responsiveness of the elicitation methods, only 12% (n=4) formally tested validity, 6% (n=2) tested reliability, and none tested responsiveness. We have summarized methods of belief elicitation for Bayesian priors. The validity, reliability, and responsiveness of elicitation methods have been infrequently evaluated. Until comparative studies are performed, strategies to reduce the effects of bias on the elicitation should be used. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                BMJ Qual Saf
                BMJ Qual Saf
                qshc
                qhc
                BMJ quality & safety
                BMJ Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-5415
                2044-5423
                December 2012
                13 September 2012
                : 21
                : Suppl_1 , Proceedings from the European Handover Research Collaborative
                : i29-i38
                Affiliations
                [1 ]Department of Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham, UK
                [2 ]Centre for Learning Sciences and Technologies, Open Universiteit Nederland, Heerlen, The Netherlands
                [3 ]Utrecht Medical Center, Utrecht, The Netherlands
                [4 ]University of Stavanger, Stavanger, Norway
                [5 ]University College Cork, Cork, Ireland
                [* ]The European HANDOVER Research Collaborative consists of: Venneri F, Molisso A (Azienda Sanitaria Firenze, Italy), Albolino S, Toccafondi G (Clinical Risk Management and Patient Safety Center, Tuscany Region, Italy), Barach P, Gademan P, Göbel B, Johnson J, Kalkman C, Pijnenborg L (Patient Safety Center, University Medical Center Utrecht, Utrecht, The Netherlands), Wollersheim H, Hesselink G, Schoonhoven L, Vernooij-Dassen M, Zegers M (Scientific Institute for Quality of Healthcare, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands), Boshuizen E, Drachsler H, Kicken W, van der Klink M, Stoyanov S (Centre for Learning Sciences and Technologies, Open University, Heerlen, The Netherlands), Kutryba B, Dudzik-Urbaniak E, Kalinowski M, Kutaj-Wasikowska H (National Center for Quality Assessment in Health Care, Krakow, Poland), Suñol R, Groene O, Orrego C (Avedis Donabedian Institute, Universidad Autónoma de Barcelona, Barcelona, Spain), Öhlén G, Airosa F, Bergenbrant S, Flink M, Hansagi H, Olsson M (Karolinska University Hospital, Stockholm, Sweden), Lilford R, Chen Y-F, Novielli N, Manaseki-Holland S (University of Birmingham, Birmingham, United Kingdom).
                Author notes
                [Correspondence to ] Dr Richard J Lilford, Department of Public Health, Epidemiology and Biostatistics, University of Birmingham, Edgbaston, West Midlands, Birmingham B15 2TT, UK; r.j.lilford@ 123456bham.ac.uk
                Article
                bmjqs-2012-001210
                10.1136/bmjqs-2012-001210
                3551195
                22976505
                0555a02a-8f3f-49fb-89c0-fe1ef7e08c9d
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/3.0/ and http://creativecommons.org/licenses/by-nc/3.0/legalcode

                History
                : 7 August 2012
                : 22 May 2012
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