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      In-hospital interventions for reducing readmissions to acute care for adults aged 65 and over: An umbrella review

      1 , 2 , 1 , 1 , 2
      International Journal for Quality in Health Care
      Oxford University Press (OUP)

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          Abstract

          Purpose

          The aim of this umbrella review was to synthesize existing systematic review evidence on the effectiveness of in-hospital interventions to prevent or reduce avoidable hospital readmissions in older people (≥65 years old).

          Data sources

          A comprehensive database search was conducted in May 2019 through MEDLINE, EMBASE, CINAHL, the JBI Database of Systematic Reviews, DARE and Epistemonikos.

          Study Selection

          Systematic reviews and other research syntheses, including meta-analyses, exploring the effectiveness of hospital-based interventions to reduce readmissions for people aged 65 and older, irrespective of gender or clinical condition, were included for review. If a review did not exclusively focus on this age group, but data for this group could be extracted, then it was considered for inclusion. Only reviews in English were included.

          Data extraction

          Data extracted for each review included the review objective, participant details, setting and context, type of studies, intervention type, comparator and findings.

          Results of data synthesis

          Twenty-nine reviews were included for analysis. Within these reviews, 11 intervention types were examined: in-hospital medication review, discharge planning, comprehensive geriatric assessment, early recovery after surgery, transitional care, interdisciplinary team care, in-hospital nutrition therapy, acute care geriatric units, in-hospital exercise, postfall interventions for people with dementia and emergency department-based palliative care. Except for discharge planning and transitional care, none of the interventions significantly reduced readmissions among older adults.

          Conclusion

          There is limited evidence to support the effectiveness of existing hospital-based interventions to reduce readmissions for people aged 65 and older.

          Related collections

          Most cited references65

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          Readmissions, Observation, and the Hospital Readmissions Reduction Program.

          The Hospital Readmissions Reduction Program, which is included in the Affordable Care Act (ACA), applies financial penalties to hospitals that have higher-than-expected readmission rates for targeted conditions. Some policy analysts worry that reductions in readmissions are being achieved by keeping returning patients in observation units instead of formally readmitting them to the hospital. We examined the changes in readmission rates and stays in observation units over time for targeted and nontargeted conditions and assessed whether hospitals that had greater increases in observation-service use had greater reductions in readmissions.
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            Ten simple rules for conducting umbrella reviews

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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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                Author and article information

                Contributors
                Journal
                International Journal for Quality in Health Care
                Oxford University Press (OUP)
                1353-4505
                1464-3677
                August 2020
                September 23 2020
                June 19 2020
                August 2020
                September 23 2020
                June 19 2020
                : 32
                : 7
                : 414-430
                Affiliations
                [1 ]College of Nursing and Health Sciences, Flinders University, Adelaide, South Australia, Australia
                [2 ]Caring Futures Institute, Flinders University, Adelaide, South Australia, Australia
                Article
                10.1093/intqhc/mzaa064
                32558919
                6717ef59-77cb-4496-a085-2d7ccec232bc
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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