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Objective: The COVID‐19 pandemic has had a substantial impact on the utilisation of hospital and emergency department (ED) services. We examined the effect of a rapid response service on hospital re‐presentations among people discharged from the ED and short‐stay wards at a tertiary referral hospital. Methods: This retrospective cohort study compared 112 patients who completed the Care in the Community program with 112 randomly selected controls. Both cases and controls were discharged from hospital between September 2020 and June 2021. Intervention patients were evaluated by a multidisciplinary team, who implemented a goal‐directed program of up to 4‐weeks duration. Logistic regression, negative binomial regression and Cox proportional hazards regression were used to evaluate outcomes at 28 days and at 6 months. Results: The median time between referral and the first home visit was 3.9 days. In adjusted analyses, the intervention reduced hospital re‐presentations at 28 days (odds ratio:.40, 95% confidence interval (CI):.17–.94) and lengthened the time to the first hospital re‐presentation (hazard ratio:.59, 95% CI:.38–.92). Although the intervention did not reduce the total number of hospital re‐presentations at 6 months (adjusted incidence rate ratio:.73, 95% CI:.49–1.08), it reduced total time spent in hospital by 303 days (582 vs. 885). Conclusions: This study is among the first to investigate the effect of a community‐based intervention on hospital re‐presentations during the COVID‐19 pandemic. It provides evidence that a sustainable 4‐week intervention is associated with reduced hospital re‐presentations and time spent in hospital. [ABSTRACT FROM AUTHOR]

Background/Objective: Ineffective interdisciplinary communication has negative impacts on patient outcomes. The use of regular structured interdisciplinary bedside rounds (SIBR), where each patient interaction lasts 3–5 minutes, is a model of care that improves interdisciplinary communication. We evaluated the impact of SIBR on in‐hospital falls. Design: Prospective before‐after study of older people hospitalized with acute illness. Setting: Two side‐by‐side aged care wards in a university hospital in Sydney, Australia. Participants: A total of 3,673 consecutive inpatients of mean age 83.8 ± 7.7 years, with 1,703 before SIBR compared with 1,970 after SIBR. No patients were excluded from potential participation. Intervention: Twice‐weekly SIBR. Measurements: Falls data were manually extracted from a mandatory institutional incident reporting database. Medical diagnoses were based on the Australian Refined Diagnosis Related Groups classification system. Injuries due to falls were corroborated using the institutional electronic medical record (Cerner PowerChart). Generalized estimating equations were used to evaluate the incidence rate ratio (IRR) of falls and fall‐related injuries. A negative binomial distribution and a logarithmic link function were used to linearize regression equations. Results: After SIBR, there were 7.4 falls per 1,000 occupied bed days (OBD), compared with 10.6 falls per 1,000 OBD before SIBR (P <.001). The implementation of SIBR reduced falls (IRR = 0.67, 95% CI = 0.52–0.85), after adjusting for age, gender, cognitive impairment, behavioral and psychological symptoms of dementia, deconditioning and frailty, but not fall‐related injuries (IRR = 0.79, 95% CI = 0.52–1.20). Conclusion: This study is the first to investigate the effect of SIBR on in‐hospital falls. It provides evidence that a sustainable, twice‐weekly intervention is associated with a reduction in falls. It has the potential to be used in other settings where falls are frequent. [ABSTRACT FROM AUTHOR]

Aim To examine the relationship between newly made medical diagnoses and length of stay ( LOS) of acutely unwell older patients. Methods Consecutive patients admitted under the care of four geriatricians were randomly allocated to a model development sample ( n = 937) or a model validation sample ( n = 855). Cox regression was used to model LOS. Variables considered for inclusion in the development model were established risk factors for LOS and univariate predictors from our dataset. Variables selected in the development sample were tested in the validation sample. Results A median of five new medical diagnoses were made during a median LOS of 10 days. New diagnoses predicted an increased LOS (hazard ratio 0.90, 95% confidence interval 0.88-0.92). Other significant predictors of increased LOS in both samples were malnutrition and frailty. Conclusions Identification of new medical diagnoses may have implications for Diagnosis Related Groups-based funding models and may improve the care of older people. [ABSTRACT FROM AUTHOR]