Background: Risk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factors could result in hospitals being unfairly penalized when they discharge patients to communities that are less able to support care transitions and disease management. While incorporating adjustments for the myriad of social and economic factors outside of the hospital setting could improve the accuracy of readmission rates as a performance measure, doing so has limited feasibility due to the number of potential variables and the paucity of data to measure them. This paper assesses a practical approach to addressing this problem: using mixed-effect regression models to estimate case-mix adjusted risk of readmission by community of patients' residence (community risk of readmission) as a complementary performance indicator to hospital readmission rates.
Methods: Using hospital discharge data and mixed-effect regression models with a random intercept for community, we assess if case-mix adjusted community risk of readmission can be useful as a quality indicator for community-based care. Our outcome of interest was an unplanned repeat hospitalisation. Our primary exposure was community of residence.
Results: Community of residence is associated with case-mix adjusted risk of unplanned repeat hospitalisation. Community risk of readmission can be estimated and mapped as indicators of the ability of communities to support both care transitions and long-term disease management.
Conclusion: Contextualising readmission rates through a community lens has the potential to help hospitals and policymakers improve discharge planning, reduce penalties to hospitals, and most importantly, provide higher quality care to the people that they serve.
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http://dx.doi.org/10.1136/bmjoq-2020-001230 | 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.
Intensive 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.
View Article and Find Full Text PDFEur J Trauma Emerg Surg
January 2025
Emergency Surgery Research Group Copenhagen (EMERGE), Department og Hepatic and Gastrointestinal Diseases, Copenhagen University Hospital- Herlev and Gentofte, Herlev, 2730, Denmark.
Purpose: Emergency laparotomy can result in a range of physical and neuropsychiatric postoperative complaints, potentially impacting quality of life. This study aimed to assess the effect of emergency laparotomy on health-related quality of life (HRQoL) and how HRQoL influences the risk of readmission.
Method: HRQoL was assessed in patients undergoing emergency laparotomy during a 1-year period.
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