Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion

• Published in: PloS one Vol. 13; no. 10; p. e0204819
• Main Authors: Rosen, Michael A, Dietz, Aaron S, Lee, Nam, Wang, I-Jeng, Markowitz, Jared, Wyskiel, Rhonda M, Yang, Ting, Priebe, Carey E, Sapirstein, Adam, Gurses, Ayse P, Pronovost, Peter J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.10.2018; Public Library of Science (PLoS)
• Subjects: Anesthesiology; Applied physics; Biology and Life Sciences; Critical care; Critical Care Nursing; Efficiency; Emergency Service, Hospital; Engineering and Technology; Engineering schools; Entropy; Feasibility studies; Generalized linear models; Health care; Hospitals; Humans; Insulin; Intensive care; Job stress; Laboratories; Level indicators; Measurement; Measurement methods; Medical personnel; Medicine; Medicine and Health Sciences; Methods; Microelectromechanical systems; Models, Theoretical; Noise; Nurses; Nursing; Nursing schools; Nursing Staff, Hospital - psychology; Patient Safety; People and Places; Physical Exertion; Physical Sciences; Practice; Prediction models; Prospective Studies; Psychological aspects; Psychometrics; Public health; Quality; Sensors; Surgery; Task Performance and Analysis; Test procedures; Variance; Workflow; Working conditions; Workload; Workload - statistics & numerical data; United States > US; Baltimore Maryland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0204819

To establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses. Repeated measures mixed-methods study in a surgical intensive care unit. Wearable and environmental sensors captured work process data. Nurses rated their mental (ME) and physical exertion (PE) for each four-hour block, and recorded patient and staffing-level workload factors. Shift was the grouping variable in multilevel modeling where sensor-based measures were used to predict nursing perceptions of exertion. There were 356 work hours from 89 four-hour shift segments across 35 bedside nursing shifts. In final models, sensor-based data accounted for 73% of between-shift, and 5% of within-shift variance in ME; and 55% of between-shift, and 55% of within-shift variance in PE. Significant predictors of ME were patient room noise (ß = 0.30, p < .01), the interaction between time spent and activity levels outside main work areas (ß = 2.24, p < .01), and the interaction between the number of patients on an insulin drip and the burstiness of speaking (ß = 0.19, p < .05). Significant predictors of PE were environmental service area noise (ß = 0.18, p < .05), and interactions between: entropy and burstiness of physical transitions (ß = 0.22, p < .01), time speaking outside main work areas and time at nursing stations (ß = 0.37, p < .001), service area noise and time walking in patient rooms (ß = -0.19, p < .05), and average patient load and nursing station speaking volume (ß = 0.30, p < .05). Analysis yielded highly predictive models of critical care nursing workload that generated insights into workflow and work design. Future work should focus on tighter connections to psychometric test development methods and expansion to a broader variety of settings and professional roles. Sensor-based measures are predictive of perceived exertion, and are viable complements to traditional task demand measures of workload.