Predicting Objective Performance Using Perceived Cognitive Workload Data in Healthcare Professionals: A Machine Learning Study.

Abstract

Cognitive Workload (CWL) is a fundamental concept in predicting healthcare professionals' (HCPs) objective performance. The study aims to compare the accuracy of the classical model (utilizes all six dimensions of the National Aeronautics and Space Administration Task Load Index (NASA-TLX)) and novel models (utilize four or five dimensions of NASA-TLX) in predicting HCPs' objective performance. We use a dataset from our previous human factors research studies and apply a broad selection of supervised machine learning classification techniques to develop data-driven computational models and predict objective performance. The study findings confirm that classical models are better predictors of objective performance than novel models. This has practical implications for research in health informatics, human factors and ergonomics, and human-computer interaction in healthcare. Findings, although promising, cannot be generalized as they are based on a small dataset. Future studies may investigate additional subjective and physiological measures of CWL to predict HCPs' objective performance.