Introduction: Globally, healthcare organizations have transitioned from paper-based documentation to electronic health records (EHR), including in Saudi Arabia. However, the adoption of EHR at the national level in Saudi Arabia needs more attention. Thus, this study aimed to determine the workflow integration of EHR and associated factors.
Objectives: The specific aims were to examine the level of EHR use and workflow integration among nurses, to determine the differences in EHR use and workflow integration based on nurses' demographic characteristics, and to determine the association between the predictive factors and EHR workflow integration.
Methods: This is a cross-sectional, correlational descriptive study. The data were collected from 293 nurses using the convenience sampling method. The participating nurses completed a questionnaire that included two measures: the Information System Use Survey and the Workflow Integration Survey (WIS). The data were analyzed using descriptive and multivariate statistics with SPSS software.
Results: The nurses had a positive perception of EHR use and workflow. The EHR use scores differed based on workplace (< .01), education level (< .05), and area of practice (< .001). Similarly, the EHR workflow integration scores varied according to workplace (< .05), education level (< .05), and area of practice (< .001). Education level and workplace significantly predicted information system use. Furthermore, education level and information system use significantly predicted the EHR integration into nurses' workflow.
Conclusion: The nurses expressed a greater perceived use of EHR regarding the integrated health information system, which was a predictor of EHR integration into nurses' workflow.
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http://dx.doi.org/10.1177/23779608241260547 | DOI Listing |
Mod Pathol
January 2025
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, Pittsburgh, PA. Electronic address:
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of medicine. Healthcare organizations are now starting to establish management strategies for integrating such platforms (AI-ML toolsets) which leverage the computational power of advanced algorithms to analyze data and to provide better insights which ultimately translates to enhanced clinical decision-making and improved patient outcomes. Emerging AI-ML platforms and trends in pathology and medicine are reshaping the field by offering innovative solutions to enhance diagnostic accuracy, operational workflows, clinical decision support, and clinical outcomes.
View Article and Find Full Text PDFJ Drugs Dermatol
January 2025
Background: The prevalence of burnout among United States (US) dermatologists has surged, reaching 49% in 2023, with a growing volume of bureaucratic tasks (eg, charting, paperwork) the leading factor behind professional fatigue. We seek to explore the competitive landscape and efficacy of AI-powered patient documentation to alleviate burnout among dermatologists by optimizing documentation practices while maintaining accuracy.
Methods: We conducted a review of eighteen AI-powered automated documentation products available in the current healthcare landscape, focusing on their integration with electronic health record (EHR) systems, HIPAA compliance, language support, mobile accessibility, and consumer type.
Cureus
December 2024
Psychiatry, The Redwoods Centre, Shrewsbury, GBR.
Background Postoperative delirium (POD) is a common and debilitating complication in elderly hip fracture patients, associated with significant clinical and functional consequences. Early identification of risk factors, such as cognitive impairment and vitamin D deficiency, is essential to mitigate its impact. However, preoperative screening practices are often inconsistent.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
October 2024
Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium.
Background And Purpose: With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy.
Materials And Methods: The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS).
J Med Microbiol
January 2025
Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, UK.
Bloodstream infections (BSIs) are one of the most serious infections investigated by microbiologists. However, the time to detect a BSI fails to meet the rapidity required to inform clinical decisions in real time. Blood culture (BC) is considered the gold standard for diagnosing bloodstream infections.
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