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      Effects of patients’ hospital discharge preferences on uptake of clinical decision support

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          Abstract

          The Centers for Medicare and Medicaid Services identified unplanned hospital readmissions as a critical healthcare quality and cost problem. Improvements in hospital discharge decision-making and post-discharge care are needed to address the problem. Utilization of clinical decision support (CDS) can improve discharge decision-making but little is known about the empirical significance of two opposing problems that can occur: (1) negligible uptake of CDS by providers or (2) over-reliance on CDS and underuse of other information. This paper reports an experiment where, in addition to electronic medical records (EMR), clinical decision-makers are provided subjective reports by standardized patients, or CDS information, or both. Subjective information, reports of being eager or reluctant for discharge, was obtained during examinations of standardized patients, who are regularly employed in medical education, and in our experiment had been given scripts for the experimental treatments. The CDS tool presents discharge recommendations obtained from econometric analysis of data from de-identified EMR of hospital patients. 38 clinical decision-makers in the experiment, who were third and fourth year medical students, discharged eight simulated patient encounters with an average length of stay 8.1 in the CDS supported group and 8.8 days in the control group. When the recommendation was “Discharge,” CDS uptake of “Discharge” recommendation was 20% higher for eager than reluctant patients. Compared to discharge decisions in the absence of patient reports: (i) odds of discharging reluctant standardized patients were 67% lower in the CDS-assisted group and 40% lower in the control (no-CDS) group; whereas (ii) odds of discharging eager standardized patients were 75% higher in the control group and similar in CDS-assisted group. These findings indicate that participants were neither ignoring nor over-relying on CDS.

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          A Behavioral Model of Rational Choice

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            Risk prediction models for hospital readmission: a systematic review.

            Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison. To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use. The databases of MEDLINE, CINAHL, and the Cochrane Library were searched from inception through March 2011, the EMBASE database was searched through August 2011, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies published in the English language of prediction models tested with medical patients in both derivation and validation cohorts. Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection. Of 7843 citations reviewed, 30 studies of 26 unique models met the inclusion criteria. The most common outcome used was 30-day readmission; only 1 model specifically addressed preventable readmissions. Fourteen models that relied on retrospective administrative data could be potentially used to risk-adjust readmission rates for hospital comparison; of these, 9 were tested in large US populations and had poor discriminative ability (c statistic range: 0.55-0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c statistic range: 0.56-0.72), and 5 could be used at hospital discharge (c statistic range: 0.68-0.83). Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health. Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly. Although in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.
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              Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings.

              To synthesize the literature on clinical decision-support systems' (CDSS) impact on healthcare practitioner performance and patient outcomes. Literature search on Medline, Embase, Inspec, Cinahl, Cochrane/Dare and analysis of high-quality systematic reviews (SRs) on CDSS in hospital settings. Two-stage inclusion procedure: (1) selection of publications on predefined inclusion criteria; (2) independent methodological assessment of preincluded SRs by the 11-item measurement tool, AMSTAR. Inclusion of SRs with AMSTAR score 9 or above. SRs were thereafter rated on level of evidence. Each stage was performed by two independent reviewers. 17 out of 35 preincluded SRs were of high methodological quality and further analyzed. Evidence that CDSS significantly impacted practitioner performance was found in 52 out of 91 unique studies of the 16 SRs examining this effect (57%). Only 25 out of 82 unique studies of the 16 SRs reported evidence that CDSS positively impacted patient outcomes (30%). Few studies have found any benefits on patient outcomes, though many of these have been too small in sample size or too short in time to reveal clinically important effects. There is significant evidence that CDSS can positively impact healthcare providers' performance with drug ordering and preventive care reminder systems as most clear examples. These outcomes may be explained by the fact that these types of CDSS require a minimum of patient data that are largely available before the advice is (to be) generated: at the time clinicians make the decisions.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                8 March 2021
                2021
                : 16
                : 3
                : e0247270
                Affiliations
                [1 ] Department of Economics and Experimental Economics Center, Georgia State University, Atlanta, Georgia, United States of America
                [2 ] Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
                [3 ] Department of Economics and Business Management, University of California – Merced, Merced, California, United States of America
                [4 ] Department of Surgery, Emory University School of Medicine, Atlanta, Georgia, United States of America
                Brown University, UNITED STATES
                Author notes

                Competing Interests: The authors declare existence of United States Letters Patent No. 10,622,099 for “Systems and Methods for Supporting Hospital Discharge Decision Making.” Applicants: Georgia State University Research Foundation, Inc. and Emory University. Inventors: James C. Cox, Vjollca Sadiraj, Kurt E. Schnier, and John F. Sweeney. Assignees: Georgia State University Research Foundation, Inc. and Emory University. The authors have no other competing interests. Nothing in the above alters our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                https://orcid.org/0000-0002-8614-9986
                https://orcid.org/0000-0001-6572-8658
                Article
                PONE-D-20-27126
                10.1371/journal.pone.0247270
                7939268
                33684144
                b714d256-600c-4976-867c-e1848b12fec2
                © 2021 Cox et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 August 2020
                : 3 February 2021
                Page count
                Figures: 1, Tables: 4, Pages: 15
                Funding
                Funded by: National Institutes of Health, National Institute on Aging
                Award ID: 1RC4AG039071
                Award Recipient :
                Funded by: National Institutes of Health, National Institute on Aging
                Award ID: 1RC4AG039071
                Award Recipient :
                Funded by: National Institutes of Health, National Institute on Aging
                Award ID: 1RC4AG039071
                Award Recipient :
                Funded by: National Institutes of Health, National Institute on Aging
                Award ID: 1RC4AG039071
                Award Recipient :
                This study was funded by the National Institutes of Health, National Institutes of Health, National Institute on Aging (grant number 1RC4AG039071): John F. Sweeney (PI); James C. Cox (PI); Vjollca Sadiraj (Co-investigator), Kurt E. Schnier (Co-investigator).
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