Dataset Information

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.

ABSTRACT:

Objective

To establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses.

Materials and methods

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.

Results

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).

Discussion

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.

Conclusions

Sensor-based measures are predictive of perceived exertion, and are viable complements to traditional task demand measures of workload.

SUBMITTER: Rosen MA

PROVIDER: S-EPMC6193584 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

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.

Rosen Michael A MA Dietz Aaron S AS Lee Nam N Wang I-Jeng IJ Markowitz Jared J Wyskiel Rhonda M RM Yang Ting T Priebe Carey E CE Sapirstein Adam A Gurses Ayse P AP Pronovost Peter J PJ

PloS one 20181012 10

<h4>Objective</h4>To establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses.<h4>Materials and methods</h4>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 vari ...[more]

PMID: 30312326

Similar Datasets

Project description:BackgroundVietnam has encountered difficulties in ensuring an adequate and equitable distribution of health workforce. The traditional staffing norms stated in the Circular 08/TT-BYT issued in 2007 based solely on population or institutional size and do not adequately take into consideration the variations of need such as population density, mortality and morbidity patterns. To address this problem, more rigorous approaches are needed to determine the number of personnel in health facilities. One such approach is Workload Indicators of Staffing Need (WISN) developed by the World Health Organization (WHO), a facility-based workforce planning method that assists managers in defining the responsibilities of different workforce categories and improving the appropriateness and efficiency of a staff mix.MethodsThis study applied the WISN approach and was employed in 22 clinical departments at four hospitals in Vietnam between 2015 and 2018. 22 targeted group discussions involving nurses were conducted. Hospital personnel records have been retrieved. The data were analyzed according to WISN instructions.ResultsOf the 22 departments, there was a shortage of 1 to 2 nurses in 10 departments, with WISN ratios ranging between 0.88 and 0.95. Only 01 clinical colleges at Can Tho Hospital lacked 05 nurses, facing a high workload with a WISN ratio of 0.78. Administrative time represented 20-40% of the total work time of a nurse. In comparison, nurses at Can Tho Hospital spent time on administration from 24 onwards. 5-41.7% of their working time while nurses at Thanh Hoa Hospital spent 21-33%.ConclusionsThe application of the WISN enabled health managers to analyze the workload of nurses, calculate staffing needs, and thus effectively contribute to the workforce planning process. It is expected that the results of this research will encourage the use of the WISN tool in other hospitals and health facilities across the health system. At provincial and national levels, this study provides important evidence to help policy makers develop guidelines for personnel norms for health facilities in the context of limited resources, while the existing regulation is no longer appropriate.

Dataset Information

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.

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

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.

Similar Datasets

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