(untitled) (The Review of Diabetic Studies, 2025)
Background: Fatigue among healthcare workers is a well-documented contributor to medical errors, compromising patient safety and clinical outcomes. Conventional mitigation strategies, including duty-hour restrictions and self-reported fatigue assessments, remain limited in their ability to provide timely, objective detection of fatigue in real-world practice. Objective: This study aimed to evaluate the effectiveness of wearable biometric sensors in detecting fatigue among healthcare workers and to examine their association with medical error incidence. Methods: A prospective cohort design was conducted in two tertiary hospitals involving 120 healthcare workers, divided into a biometric monitoring group and a control group. Participants in the intervention arm wore multimodal devices measuring heart rate variability, skin conductance, actigraphy, and cognitive reaction time during clinical shifts. Fatigue episodes were defined using physiological thresholds and cross-validated against self-reported sleepiness scales. Medical errors were recorded via electronic health records, incident reporting systems, and observer logs. Statistical analysis incorporated descriptive comparisons, machine learning models, and regression testing. Results: The wearable monitoring system demonstrated high predictive accuracy for fatigue detection (LSTM AUC = 0.91, sensitivity = 88.1%). The biometric group reported 51% fewer documented medical errors compared to controls, with the most significant improvements observed in medication safety and charting accuracy. Night-shift nurses exhibited the highest rates of fatigue and error reduction following biometric alerts. Conclusion: Wearable fatigue monitoring offers a robust and scalable tool for early identification of fatigue and prevention of medical errors. Its integration into hospital safety systems could strengthen workforce resilience and improve patient care quality.