Background: Localization of general medical inpatient teams is an attractive way to improve inpatient care but has not been adequately studied.
Objective: To evaluate the impact of localizing general medical teams to a single nursing unit.
Design: Quasi-experimental study using historical and concurrent controls.
Setting: A 490-bed academic medical center in the midwestern United States.
Patients: Adult, general medical patients, other than those with sickle cell disease, admitted to medical teams staffed by a hospitalist and a physician assistant (PA).
Intervention: Localization of patients assigned to 2 teams to a single nursing unit.
Measurements: Length of stay (LOS), 30-day risk of readmission, charges, pages to teams, encounters, relative value units (RVUs), and steps walked by PAs.
Results: Localized teams had 0.89 (95% confidence interval [CI], 0.37-1.41) more patient encounters and generated 2.20 more RVUs per day (CI, 1.10-3.29) compared to historical controls; and 1.02 (CI, 0.46-1.58) more patient encounters and generated 1.36 more RVUs per day (CI, 0.17-2.55) compared to concurrent controls. Localized teams received 51% (CI, 48-54) fewer pages during the workday. LOS may have been approximately 10% higher for localized teams. Risk of readmission within 30 days and charges incurred were no different. PAs possibly walked fewer steps while localized.
Conclusion: Localization of medical teams led to higher productivity and better workflow, but did not significantly impact readmissions or charges. It may have had an unintended negative impact on hospital efficiency; this finding deserves further study.
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http://dx.doi.org/10.1002/jhm.1948 | DOI Listing |
Biomed Phys Eng Express
January 2025
National School of Electronics and Telecommunication of Sfax, Sfax rte mahdia, sfax, sfax, 3012, TUNISIA.
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification.
View Article and Find Full Text PDFAnn Intern Med
January 2025
Durham VA Health Care System, Durham; and Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina (K.M.G.).
Background: Tissue-based genomic classifiers (GCs) have been developed to improve prostate cancer (PCa) risk assessment and treatment recommendations.
Purpose: To summarize the impact of the Decipher, Oncotype DX Genomic Prostate Score (GPS), and Prolaris GCs on risk stratification and patient-clinician decisions on treatment choice among patients with localized PCa considering first-line treatment.
Data Sources: MEDLINE, EMBASE, and Web of Science published from January 2010 to August 2024.
J Med Internet Res
January 2025
Medical Information Department, Civil Hospices of Lyon, Lyon, France.
J Med Internet Res
January 2025
Division of Nephrology and Endocrinology, The University of Tokyo, Tokyo, Japan.
J Particip Med
January 2025
Division of Allergy & Pulmonary Medicine, Washington University School of Medicine, St Louis, MO, United States.
Background: Adolescents and young adults (AYA) with cystic fibrosis (CF) are at risk for deviating from their daily treatment regimen due to significant time burden, complicated daily therapies, and life stressors. Developing patient-centric, effective, engaging, and practical behavioral interventions is vital to help sustain therapeutically meaningful self-management.
Objective: This study aimed to devise and refine a patient-centered telecoaching intervention to foster self-management in AYA with CF using a combination of intervention development approaches, including an evidence- and theory-based approach (ie, applying existing theories and research evidence for behavior change) and a target population-centered approach (ie, intervention refinement based on the perspectives and actions of those individuals who will use it).
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