Objective To assess the relationship between (1) task demands and workload, (2) task demands and performance, and (3) workload and performance, all during physician-computer interactions in a simulated environment.#N##N#Methods Two experiments were performed in 2 different electronic medical record (EMR) environments: WebCIS ( n  = 12) and Epic ( n  = 17). Each participant was instructed to complete a set of prespecified tasks on 3 routine clinical EMR-based scenarios: urinary tract infection (UTI), pneumonia (PN), and heart failure (HF). Task demands were quantified using behavioral responses (click and time analysis). At the end of each scenario, subjective workload was measured using the NASA-Task-Load Index (NASA-TLX). Physiological workload was measured using pupillary dilation and electroencephalography (EEG) data collected throughout the scenarios. Performance was quantified based on the maximum severity of omission errors.#N##N#Results Data analysis indicated that the PN and HF scenarios were significantly more demanding than the UTI scenario for participants using WebCIS ( P  < .01), and that the PN scenario was significantly more demanding than the UTI and HF scenarios for participants using Epic ( P  < .01). In both experiments, the regression analysis indicated a significant relationship only between task demands and performance ( P  < .01).#N##N#Discussion Results suggest that task demands as experienced by participants are related to participants' performance. Future work may support the notion that task demands could be used as a quality metric that is likely representative of performance, and perhaps patient outcomes.#N##N#Conclusion The present study is a reasonable next step in a systematic assessment of how task demands and workload are related to performance in EMR-evolving environments.
['Lukasz M. Mazur', 'Prithima Mosaly', 'Carlton Moore', 'Elizabeth Comitz', 'Fei Yu', 'Aaron D. Falchook', 'M.J. Eblan', 'Lesley Hoyle', 'Gregg Tracton', 'Bhishamjit S. Chera', 'Lawrence B. Marks']
Toward a better understanding of task demands, workload, and performance during physician-computer interactions