Background: Remote patient monitoring (RPM) clinical trials have reported mixed results in improving outcomes for patients with chronic heart failure (HF). The impact of clinical workflows that could impact RPM effectiveness is often overlooked. We sought to characterize workflows and response protocols that could impact outcomes in studies of noninvasive RPM in HF.
Methods: We reviewed studies (1999-2024) assessing noninvasive RPM interventions for adults with HF. We collected 24 aspects of workflows describing education, physiologic and symptomatic data collection, transmission and review, clinical escalation protocols, and response time. We attempted to perform a meta-analysis to identify associations between workflow components and outcomes of death and hospitalization.
Results: We identified 63 studies (57.1% randomized controlled, 23.8% pilot/feasibility, 19.1% other) comprising 16,699 subjects. Despite a large number of studies and subjects, workflow reporting was insufficient to perform our intended meta-analysis regarding key workflow components. RPM clinical workflows were diverse in configuration, with high variability in component description ranging from always reported to never reported. Specifics of monitoring devices and related training were well reported as expected based on most trial hypotheses. However, elements of clinical data response such as frequency of data review, clinical escalation criteria, and provider response time were often underreported or not reported at all (48%, 24%, and 97%, respectively), hindering study replication and evidence-based implementation.
Conclusions: Clinical workflows are poorly described in noninvasive RPM studies, preventing systematic assessment, device comparison, and replication. A standardized approach to reporting HF RPM workflows is vital to evaluate effectiveness and guide evidence-based clinical implementation.
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http://dx.doi.org/10.1016/j.cardfail.2024.11.012 | DOI Listing |
Cancer Imaging
January 2025
Melbourne Theranostic Innovation Centre, Level 8, 14-20 Blackwood St, North Melbourne, VIC, 3051, Australia.
True total-body and extended axial field-of-view (AFOV) PET/CT with 1m or more of body coverage are now commercially available and dramatically increase system sensitivity over conventional AFOV PET/CT. The Siemens Biograph Vision Quadra (Quadra), with an AFOV of 106cm, potentially allows use of significantly lower administered radiopharmaceuticals as well as reduced scan times. The aim of this study was to optimise acquisition protocols for routine clinical imaging with FDG on the Quadra the prioritisation of reduced activity given physical infrastructure constraints in our facility.
View Article and Find Full Text PDFEvid Based Dent
January 2025
Division of Periodontics, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India.
Design: A triple-armed, double-blind randomized controlled trial with cross-over design investigated patient-reported satisfaction and objective dental evaluation of a 3-unit, monolithic zirconium dioxide (ZrO2), implant-supported fixed dental prosthesis (iFDP) fabricated with 2 completely digital workflows and 1 mixed analog-digital workflow.
Case Selection: Participants enrolled required rehabilitation of 2 dental implants in posterior region of either of the arches with a 3-unit, ZrO2 iFDP. A total of 20 participants received the 3 types of ZrO2, iFDP fabricated by 3 different methods.
PLOS Glob Public Health
January 2025
Center for Global Health, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Humanitarian medical response to natural and human-made disasters can be complicated by high clinician, staff, and patient turnover. While electronic medical records are being scaled up globally, their use remains limited in humanitarian response settings. The Fast Electronic Medical Record (fEMR) system is an open-source electronic health record system specifically designed for use in resource-limited settings and humanitarian crises.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Language Intelligence and Information Retrieval (LIIR) Lab, Department of Computer Science, KU Leuven, Leuven, Belgium.
The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for efficient data mining. Natural language processing (NLP) methods have been pivotal in this endeavor, aiming to extract crucial clinical concepts embedded within free-form text.
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