Background: Little is known about what predicts disengagement from rehabilitation treatment for people affected by severe and persistent mental illness (SPMI).
Aims: To identify predictors of unplanned discharge among consumers admitted to community-based residential rehabilitation units in Australia.
Method: Secondary analysis of data from a prospective cohort study of consumers admitted to three Community Care Units (CCUs) between 2014 and 2017 ( = 139). CCUs provide transitional residential rehabilitation support to people affected by SPMI. Demographic, treatment-related and clinical predictors of unplanned discharge were identified using binomial regression models controlling for site-level variability. Factors associated with self- vs staff-initiated unplanned discharge were also examined.
Results: 38.8% of consumers experienced unplanned discharge. Significant predictors of unplanned discharge were younger age, higher alcohol consumption and disability associated with mental illness, as well as recovery stage indicating a sense of growth and higher competence in daily task performance. 63.0% of unplanned discharges were initiated by staff, mostly for substance-related reasons (55.9%). History of trauma was more likely among consumers with self-initiated discharge than those with staff-initiated unplanned and planned discharge.
Conclusions: Assertive intervention to address alcohol-use, and ensuring care is trauma-informed, may assist in reducing rates of unplanned discharge from rehabilitation care.
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http://dx.doi.org/10.1080/09638237.2020.1755025 | DOI Listing |
BMJ Open
January 2025
Department of Geriatrics, Radboudumc, Nijmegen, The Netherlands.
Objective: Older adults are prone to unplanned emergency department (ED) return visits (URVs). Knowledge about patient perspectives on the preventability and reasons for these URVs is limited and lacks a representable ED study population. This study aims to determine the proportion of URVs and to explore the preventability and underlying causes as perceived by a wide range of older adults and their caregivers.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States of America.
Transitional care management (TCM) visits have been shown to reduce 30-day readmissions, but it is unclear whether the decrease arises from the TCM visit itself or from clinic-level changes to meet the requirements of the TCM visits. We conducted a cross-sectional analysis using data from Northwell Health to examine the association between the type of post-discharge follow-up visits (TCM visits versus non-TCM visits based on billing) and 30-day readmission. Furthermore, we assessed whether being seen by a provider who frequently utilizes TCM visits or the TCM visit itself was associated with 30-day readmission.
View Article and Find Full Text PDFBMJ Open
December 2024
Health Services, University of Washington, Seattle, Washington, USA.
Introduction: Ineffective coordination during care transitions from hospitals to skilled nursing facilities (SNFs) costs Medicare US$2.8-US$3.4 billion annually and results in avoidable adverse events.
View Article and Find Full Text PDFWorld J Surg
December 2024
Division of Acute Care Surgery, University of Southern California, Los Angeles, California, USA.
Background: Trauma and pregnancy are both risk factors for venous thromboembolism (VTE). We hypothesized that pregnant blunt trauma patients would have a higher incidence of VTE complications compared with matched nonpregnant females.
Methods: We conducted a retrospective cohort study using National Trauma Data Bank data from 2017 to 2022.
J Pers Med
December 2024
Division of Neurotology and Skull Base Surgery, Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, CA 92697, USA.
This study aimed to develop a machine learning (ML) algorithm that can predict unplanned reoperations and surgical/medical complications after vestibular schwannoma (VS) surgery. All pre- and peri-operative variables available in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (n = 110), except those directly related to our outcome variables, were used as input variables. A deep neural network model consisting of seven layers was developed using the Keras open-source library, with a 70:30 breakdown for training and testing.
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