Hospital readmission is an indicator of care quality. Studies have been conducted to test whether post-discharge transitional care programs can reduce hospital readmission, but results are not conclusive. The contemporary development of post-discharge support advocates a health and social partnership approach. There is a paucity of experimental studies examining the effects of such efforts. This study designed a health-social transitional care management program (HSTCMP) and subjected it to empirical testing using a randomized controlled trial in the medical units of an acute general hospital with 1700 beds in Hong Kong during the period of February 2009 to July 2010. Results using per-protocol analysis revealed that the HSTCMP significantly reduced readmission at 4-weeks (study 4.0%, control 10.2%, χ(2) = 7.98, p = 0.005). The intention-to-treat result also showed a lower readmission rate with the study group but the result was not significant (study 11.5%, control 14.7%, χ(2) = 1.53, p = 0.258). There was however significant improvement in quality of life, self-efficacy and satisfaction in the study group in both per-protocol and intention-to-treat analyses. The study suggests that a health-social partnership, using volunteers as substitutes for some of the professional care, may be effective for general medical patients.
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http://dx.doi.org/10.1016/j.socscimed.2011.06.036 | DOI Listing |
J Surg Res
January 2025
Section of Surgical Sciences, Vanderbilt University Medical Center, Nashville, Tennessee. Electronic address:
Introduction: Unplanned, delayed readmissions (>30 ds) following oncologic surgeries can increase mortality and care costs and affect hospital quality indices. However, there is a dearth of literature on rectal cancer surgery. Hence, we aimed to assess the risk factors associated with delayed readmissions following rectal cancer surgery to improve targeted interventions, patient outcomes, and quality indices.
View Article and Find Full Text PDFQJM
January 2025
School of Nursing and Advanced Practice, Liverpool John Moores University, Liverpool, United Kingdom.
Background: Contemporary stroke care is moving towards more holistic and patient-centred integrated approaches, however, there is need to develop high quality evidence for interventions that benefit patients as part of this approach.
Aim: This study aims to identify the types of integrated care management strategies that exist for people with stroke, to determine whether stroke management pathways impact patient outcomes, and to identify elements of integrated stroke care that were effective at improving outcomes.
Design: Systematic review with meta-analysis.
J Obstet Gynaecol Res
February 2025
Department of Gynaecology, Yixing People's Hospital, Yixing, China.
Aim: To examine the prognostic impact of textbook oncologic outcome (TOO) in patients with advanced ovarian cancer undergoing primary chemotherapy, along with identifying the risk factors for TOO failure.
Methods: Patients who underwent neoadjuvant chemotherapy followed by interval debulking surgery for advanced ovarian cancer at a tertiary center between 2014 and 2019 were retrospectively reviewed. TOO was defined as complete cytoreduction, no severe complications, no prolonged hospital stay, no readmission, no delayed initiation of adjuvant chemotherapy, and no 90-day mortality.
Background: Single anastomosis duodeno-ileal bypass (SADI) has emerged as a safe and effective bariatric procedure. Its simplicity and robust weight loss outcomes have contributed to its increasing popularity. While traditionally performed as an inpatient procedure, recent trends towards ambulatory surgery have prompted interest in outpatient SADI.
View Article and Find Full Text PDFIntensive Care Med Exp
January 2025
Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.
Background: The discharge practices from the intensive care unit exhibit heterogeneity and the recognition of eligible patients for discharge is often delayed. Recognizing the importance of safe discharge, which aims to minimize readmission and mortality, we developed a dynamic machine-learning model. The model aims to accurately identify patients ready for discharge, offering a comparison of its effectiveness with physician decisions in terms of safety and discrepancies in discharge readiness assessment.
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