Objectives: To summarize the research literature describing the outcomes of computerized decision support systems (CDSSs) implemented in nursing homes (NHs).
Design: Scoping review.
Methods: Search of relevant articles published in the English language between January 1, 2000, and February 29, 2020, in the Medline database. The quality of the selected studies was assessed according to PRISMA guidelines and the Mixed Method Appraisal Tool.
Results: From 1828 articles retrieved, 24 studies were selected for review, among which only 6 were randomized controlled trials. Although clinical outcomes are seldom studied, some studies show that CDSSs have the potential to decrease pressure ulcer incidence and malnutrition prevalence. Improvement of process outcomes such as increased compliance with practice guidelines, better documentation of nursing assessment, improved teamwork and communication, and cost saving, also are reported.
Conclusions And Implications: Overall, the use of CDSSs in NHs may be effective to improve patient clinical outcomes and health care delivery; however, most of the retrieved studies were observational studies, which significantly weakens the evidence. High-quality studies are needed to investigate CDSS effects and limitations in NHs.
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http://dx.doi.org/10.1016/j.jamda.2021.01.080 | DOI Listing |
J Otol
July 2024
Department of Audiology, All India Institute of Speech and Hearing, Mysuru, Karnataka, India.
Purpose: The present systematic review examined imaging findings in the Auditory Neuropathy Spectrum Disorder (ANSD) population.
Methods: Electronic databases such as Pub Med, Google Scholar, J Gate, and Science Direct were used to conduct a literature search. The articles retrieved through the literature search were assessed in two stages.
Infect Control Hosp Epidemiol
December 2024
Rush University Medical Center, ChicagoIL, USA.
Background: Diagnostic stewardship of urine cultures from patients with indwelling urinary catheters may improve diagnostic specificity and clinical relevance of the test, but risk of patient harm is uncertain.
Methods: We retrospectively evaluated the impact of a computerized clinical decision support tool to promote institutional appropriateness criteria (neutropenia, kidney transplant, recent urologic surgery, or radiologic evidence of urinary tract obstruction) for urine cultures from patients with an indwelling urinary catheter. The primary outcome was a change in catheter-associated urinary tract infection (CAUTI) rate from baseline (34 mo) to intervention period (30 mo, including a 2-mo wash-in period).
Heliyon
December 2024
School of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Land use planning tools are essential for effective land management. However, existing research has not thoroughly explored the evolution and future potential of these tools. This study addresses this gap through comprehensive literature review and data collection, mapping the progression of land use planning tools from their inception to their future trajectories.
View Article and Find Full Text PDFJ Bone Oncol
February 2025
Center of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Unlabelled: Bone metastasis from breast cancer significantly elevates patient morbidity and mortality, making early detection crucial for improving outcomes. This study utilizes radiomics to analyze changes in the thoracic vertebral bone marrow microenvironment from chest computerized tomography (CT) images prior to bone metastasis in breast cancer, and constructs a model to predict metastasis.
Methods: This study retrospectively gathered data from breast cancer patients who were diagnosed and continuously monitored for five years from January 2013 to September 2023.
Algorithms
December 2023
Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain.
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