ANALYSIS OF UPPER EXTREMITY RISK USING ARTIFICIAL INTELLIGENCE AMONG HOSPITAL PHARMACISTS: A PILOT STUDY

Abstract

Hospital pharmacists work in high-pressure environments and are exposed to tasks that increase the risk of musculoskeletal disorders (MSDs), particularly due to repetitive movements and awkward postures such as overhead reaching and frequent bending during prescription handling and medication dispensing. This descriptive study aimed to assess ergonomic risks among hospital pharmacists using an artificial intelligence (AI)-based system in combination with the Rapid Upper Limb Assessment (RULA) method for posture evaluation based on motion-captured images. The sample consisted of nine outpatient pharmacists. Most participants were female (88.9%), with a mean age of 29.9 ± 4.7 years and an average work experience of 3.8 ± 2.0 years. All participants reported musculoskeletal discomfort affecting daily activities. The AI-based assessment indicated an overall moderate level of ergonomic risk. By body region, the upper arm had the highest risk level, classified as moderate. The Wilcoxon signed-rank test showed no significant differences in risk levels between the left and right sides of the body (p > 0.05). In conclusion, hospital pharmacists face a moderate level of ergonomic risk, with overhead work and repetitive tasks as key contributing factors. Ergonomic interventions, including workstation redesign and posture improvement, are recommended to reduce long-term MSD risk.

Keywords: Upper Body Posture Risk Assessment, AI-Power Risk Assessment, Pharmacists