This two-arm randomized controlled trial evaluated the impact of a Stepped-Care intervention (predictive analytics combined with tailored interventions) on the healthcare costs of older adults using a Personal Emergency Response System (PERS). A total of 370 patients aged 65 and over with healthcare costs in the middle segment of the cost pyramid for the fiscal year prior to their enrollment were enrolled for the study. During a 180-day intervention period, control group (CG) received standard care, while intervention group (IG) received the Stepped-Care intervention. The IG had 31% lower annualized inpatient cost per patient compared with the CG (3.7 K, $8.1 K vs. $11.8 K, p = 0.02). Both groups had similar annualized outpatient costs per patient ($6.1 K vs. $5.8 K, p = 0.10). The annualized total cost reduction per patient in the IG vs. CG was 20% (3.5 K, $17.7 K vs. $14.2 K, p = 0.04). Predictive analytics coupled with tailored interventions has great potential to reduce healthcare costs in older adults, thereby supporting population health management in home or community settings.
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http://dx.doi.org/10.1038/s41746-021-00449-w | DOI Listing |
J Struct Biol
January 2025
Center of Structural Biology, Vanderbilt University, Nashville, TN, USA; Department of Chemistry, Vanderbilt University, Nashville, TN, USA; Institute for Drug Discovery, Institute for Computer Science, Wilhelm Ostwald Institute for Physical and Theoretical Chemistry, University Leipzig, Leipzig, Germany; Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI and School of Embedded Composite Artificial Intelligence SECAI, Dresden/Leipzig, Germany; Department of Pharmacology, Institute of Chemical Biology, Center for Applied Artificial Intelligence in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Electronic address:
High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid epitope mapping, but its data are too sparse for independent structure determination. In this study, we introduce RosettaHDX, a hybrid method that combines computational docking with differential HDX-MS data to enhance the accuracy of antibody-antigen complex models beyond what either method can achieve individually.
View Article and Find Full Text PDFCureus
December 2024
Urology, SSM Health Saint Louis University Hospital, Saint Louis, USA.
Introduction Fournier's gangrene (FG) is a rapidly progressing necrotizing fasciitis. The Fournier's Gangrene Severity Index (FGSI), in conjunction with the Charlson Comorbidity Index (CCI), has been used as a mortality predictor during hospitalization. Patients with diabetes have also been shown to be at an increased risk for the development of FG.
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December 2024
General Surgery, Sri Devaraj Urs Medical College, Kolar, IND.
Introduction Acute appendicitis is a common surgical emergency that requires a timely and accurate diagnosis to prevent complications. Several laboratory markers have been assessed to improve the diagnostic accuracy of acute appendicitis, including C-reactive protein (CRP), white blood cell (WBC) count, and cytokines like interleukins and tumor necrosis factor-alpha. One less commonly used but potentially valuable marker is the mean platelet volume (MPV), which indicates the size of circulating platelets and has the potential to serve as a biomarker for inflammatory conditions.
View Article and Find Full Text PDFGiant cell arteritis (GCA), a systemic vasculitis affecting large and medium-sized arteries, poses significant diagnostic and management challenges, particularly in preventing irreversible complications like vision loss. Recent advancements in artificial intelligence (AI) technologies, including machine learning (ML) and deep learning (DL), offer promising solutions to enhance diagnostic accuracy and optimize treatment strategies for GCA. This systematic review, conducted according to the PRISMA 2020 guidelines, synthesizes existing literature on AI applications in GCA care, with a focus on diagnostic accuracy, treatment outcomes, and predictive modeling.
View Article and Find Full Text PDFEClinicalMedicine
August 2024
Division of Cancer Prevention and Population Sciences, Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Background: Lung cancer screening recommendations employ annual frequency for eligible individuals, despite evidence that it may not be universally optimal. The impact of imposing a structure on the screening frequency remains unknown. The ENGAGE framework, a validated framework that offers fully dynamic, analytically optimal, personalised lung cancer screening recommendations, could be used to assess the impact of screening structure on the effectiveness and efficiency of lung cancer screening.
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