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      Quality of care transition, patient safety incidents, and patients’ health status: a structural equation model on the complexity of the discharge process

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          Abstract

          Background

          The transition of patients between care contexts poses patient safety risks. Discharges to home from inpatient care can be associated with adverse patient outcomes. Quality in discharge processes is essential in ensuring safe transitions for patients. Current evidence relies on bivariate analyses and neglects contextual factors such as treatment and patient characteristics and the interactions of potential outcomes. This study aimed to investigate the associations between the quality and safety of the discharge process, patient safety incidents, and health-related outcomes after discharge, considering the treatments’ and patients’ contextual factors in one comprehensive model.

          Methods

          Patients at least 18 years old and discharged home after at least three days of inpatient treatment received a self-report questionnaire. A total of N = 825 patients participated. The assessment contained items to assess the quality and safety of the discharge process from the patient’s perspective with the care transitions measure (CTM), a self-report on the incidence of unplanned readmissions and medication complications, health status, and sociodemographic and treatment-related characteristics. Statistical analyses included structural equation modeling (SEM) and additional analyses using logistic regressions.

          Results

          Higher quality of care transition was related to a lower incidence of medication complications (B = -0.35, p < 0.01) and better health status (B = 0.74, p < 0.001), but not with lower incidence of readmissions (B = -0.01, p = 0.39). These effects were controlled for the influences of various sociodemographic and treatment-related characteristics in SEM. Additional analyses showed that these associations were only constant when all subscales of the CTM were included.

          Conclusions

          Quality and safety in the discharge process are critical to safe patient transitions to home care. This study contributes to a better understanding of the complex discharge process by applying a model in which various contextual factors and interactions were considered. The findings revealed that high quality discharge processes are associated with a lower likelihood of patient safety incidents and better health status at home even, when sociodemographic and treatment-related characteristics are taken into account. This study supports the call for developing individualized, patient-centered discharge processes to strengthen patient safety in care transitions.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12913-024-11047-3.

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          Most cited references47

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

            Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September, 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies.A detailed explanation and elaboration document is published separately and is freely available on the websites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE statement will contribute to improving the quality of reporting of observational studies
              • Record: found
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              Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares.

              In confirmatory factor analysis (CFA), the use of maximum likelihood (ML) assumes that the observed indicators follow a continuous and multivariate normal distribution, which is not appropriate for ordinal observed variables. Robust ML (MLR) has been introduced into CFA models when this normality assumption is slightly or moderately violated. Diagonally weighted least squares (WLSMV), on the other hand, is specifically designed for ordinal data. Although WLSMV makes no distributional assumptions about the observed variables, a normal latent distribution underlying each observed categorical variable is instead assumed. A Monte Carlo simulation was carried out to compare the effects of different configurations of latent response distributions, numbers of categories, and sample sizes on model parameter estimates, standard errors, and chi-square test statistics in a correlated two-factor model. The results showed that WLSMV was less biased and more accurate than MLR in estimating the factor loadings across nearly every condition. However, WLSMV yielded moderate overestimation of the interfactor correlations when the sample size was small or/and when the latent distributions were moderately nonnormal. With respect to standard error estimates of the factor loadings and the interfactor correlations, MLR outperformed WLSMV when the latent distributions were nonnormal with a small sample size of N = 200. Finally, the proposed model tended to be over-rejected by chi-square test statistics under both MLR and WLSMV in the condition of small sample size N = 200.

                Author and article information

                Contributors
                matthias.marsall@ukbonn.de
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                3 May 2024
                3 May 2024
                2024
                : 24
                : 576
                Affiliations
                [1 ]Institute for Patient Safety (IfPS), University Hospital Bonn, ( https://ror.org/01xnwqx93) Bonn, Germany
                [2 ]University Hospital Bonn, ( https://ror.org/01xnwqx93) Bonn, Germany
                [3 ]Clinic for Psychosomatic Medicine and Psychotherapy, LVR–University Hospital Essen, University of Duisburg-Essen, ( https://ror.org/04mz5ra38) Essen, Germany
                [4 ]Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University of Duisburg-Essen, ( https://ror.org/04mz5ra38) Essen, Germany
                Article
                11047
                10.1186/s12913-024-11047-3
                11069201
                38702719
                55c0e2ba-22c9-4b03-91bb-23ce3975bf45
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 April 2023
                : 25 April 2024
                Funding
                Funded by: Universitätsklinikum Bonn (8930)
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Health & Social care
                transitional safety,care transitions measure,structural equation model,readmission,medication safety,discharge,patient-reported experience measure,patient-reported outcomes measures

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