The Medicare hospital value-based purchasing (HVBP) program that links Medicare payments to quality of care became effective in 2013 in the United States. Hospital efficiency will be added to the HVBP in 2015. It is unclear whether hospital efficiency-specific hospital characteristics are associated with HVBP performance scores and the subsequent incentive payments. Using data from the American Hospital Association Annual Survey the Medicare Hospital Compare, this article examines the association of hospital efficiency hospital characteristics with the HVBP performance scores. The results indicate that less efficient hospitals are more likely to have lower patient satisfaction scores and total performance scores compared with more efficient hospitals. Hospital size, ownership, and payer mix also have significant impact on HVBP performance scores. The findings of this study provide significant policy practice implications. On the one hand, hospitals should consider investing their limited resources into identifying implementing the most cost-effective procedures to improve their patient experience total performance scores. On the other hand, policymakers should consider the unintended negative impact that these new payment incentives will likely have on hospitals that serve a higher proportion of low-income racial ethnic minority populations.
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PLoS One
January 2025
Institute of Physiotherapy, FH Joanneum University of Applied Sciences, Graz, Austria.
The impact of cognitive decline in older adults can be evaluated with dual-task gait (DTG) testing in which a cognitive task is performed during walking, leading to increased costs of gait. Previous research demonstrated that higher DTG costs correlate with increasing cognitive deficits and with age. The present study was conducted to explore whether the relationship between the DTG costs and cognitive abilities in older individuals is influenced by sex differences.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marquês de São Vicente Street, Gávea, Rio de Janeiro, RJ 22451-900, Brazil.
Machine learning approaches often involve evaluating a wide range of models due to various available architectures. This standard strategy can lead to a lack of depth in exploring established methods. In this study, we concentrated our efforts on a single deep learning architecture type to assess whether a focused approach could enhance performance in fault diagnosis.
View Article and Find Full Text PDFPLoS One
January 2025
School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
The incidence of acute myeloid leukemia (AML) is increasing annually, and timely diagnostic and treatments can substantially improve patient survival rates. AML typing traditionally relies on manual microscopy for classifying and counting myeloid cells, which is time-consuming, laborious, and subjective. Therefore, developing a reliable automated model for myeloid cell classification is imperative.
View Article and Find Full Text PDFRheumatology (Oxford)
January 2025
Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Laboratory, Guangzhou, 510515, China.
Objectives: The relationship between proteomic profiles and incident systemic lupus erythematosus (SLE) remains unclear. We aimed to identify candidate plasma proteins for SLE risk in women, discover potential treatment targets for SLE, and develop and validate a protein-based prediction model for SLE risk.
Methods: 28 220 women from the UK Biobank were randomly split into training (70%) and testing (30%) sets.
J Am Med Inform Assoc
January 2025
Institute of Intelligent Rehabilitation Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Background: With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid demand for intelligent systems that can recommend comprehensive long-term care plans for older adults with disabilities who have stable clinical conditions.
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