Ergonomic design for optimizing work-related strains and enhancing patient safety in the healthcare environment.
Background: Work-related musculoskeletal disorders (MSDs) pose a significant occupational health challenge for healthcare professionals, affecting both workforce efficiency and patient safety. The physical demands of healthcare roles, particularly post-COVID-19, have increased strain on workers, nec...
| Publicado en: | Work Vol. 82; no. 2; pp. 501 - 516 |
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| Autores principales: | , , , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188321254&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188321254 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: Oct2025 vid: 82 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 188321254 185528651 188321254 188321254 10.1177/10519815251346478 188321254 ppf: 501 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ergonomic design for optimizing work-related strains and enhancing patient safety in the healthcare environment. aug: au: Azyabi, Abdulmajeed Khamaj, Abdulrahman Ali, Abdulelah M Alghamdi, Saleh Y Hamzi, Ahmed Ghandourah, Emad Ahmad, Mohammad Tauheed affil: Industrial Engineering Department, College of Engineering & Computer Science, Jazan University, Jazan, Saudi Arabia sug: subj: Health Facility Environment Occupational Diseases Prevention and Control Musculoskeletal Diseases Prevention and Control Ergonomics Evaluation Patient Safety Prediction Models Random Forest Risk Assessment Funding Source Human Questionnaires Conceptual Framework Administrative Personnel Occupational Health COVID-19 Machine Learning Algorithms Data Analysis Software Descriptive Statistics ab: Background: Work-related musculoskeletal disorders (MSDs) pose a significant occupational health challenge for healthcare professionals, affecting both workforce efficiency and patient safety. The physical demands of healthcare roles, particularly post-COVID-19, have increased strain on workers, necessitating advanced ergonomic solutions. Traditional ergonomic assessment methods often fail to provide comprehensive, data-driven insights, highlighting the need for a more integrated approach. Objective: This study aims to develop a novel Data Envelopment Analysis (DEA) and Random Forest (RF) modeling framework to enhance ergonomic risk assessment in healthcare environments. By integrating DEA's efficiency evaluation with RF's predictive modeling, the proposed methodology seeks to provide a more precise, scalable, and data-driven solution for optimizing ergonomic design and improving patient safety. Methods: The DEA-RF framework systematically evaluates ergonomic effectiveness using DEA, while RF enhances prediction accuracy, enabling proactive risk mitigation. The model was tested on real-world ergonomic data, and its performance was assessed based on accuracy, precision, recall, F-measure, error rate, and computational efficiency. Results: The model demonstrated superior performance, achieving 98.96% accuracy, 99.27% precision, 98.87% recall, and a 98.82% F-measure, with a low error rate of 1.07% and computational efficiency of 2.2 s. These findings validate the reliability and real-world applicability of the proposed framework in reducing MSD risks and improving patient safety. Conclusions: The study presents a scalable and adaptable evidence-based ergonomic assessment approach for healthcare administrators, facility designers, and policymakers. By integrating efficiency evaluation with predictive analytics, the DEA-RF framework advances ergonomic assessment methodologies, setting a foundation for future intelligent, data-driven occupational health strategies. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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