Estimating Nurse Workload Using a Predictive Model From Routine Hospital Data: Algorithm Development and Validation

Abstract

Background: Managing nurse staffing is complex due to fluctuating demand based on ward occupancy, patient acuity, and dependency. Monitoring staffing adequacy in real time has the potential to inform safe and efficient deployment of staff. Patient classification systems (PCSs) are being used for per shift workload measurement, but they add a frequent administrative task for ward nursing staff.

Objective: The objective of this study is to explore whether an algorithm could estimate ward workload using existing routinely recorded data.

Methods: Anonymized admission records and assessments from a PCS supporting the safer nursing care tool were used to determine nursing care demand in medical and surgical wards in a single UK hospital between February 2017 and February 2020. Records were linked by ward and time. The data were split into a training set (75%) and a test set (25%). We built a predictive model of ward workload (as measured by the PCS) using routinely recorded administrative data and admission National Early Warning Score. The outcome variable was ward workload derived from the patient classifications, measured as the number of whole-time equivalent (WTE) nursing staff per patient.

Results: In a test set of 11,592 ward assessments from 42 wards with a mean WTE per patient of 1.64, the model's mean absolute error was 0.078, with a mean percentage error of 4.9%. A Bland-Altman plot of the differences between the predicted values and the assessment values showed 95% of them within 0.21 WTE per patient.

Conclusions: Predictions of nursing workload from a relatively small number of routinely collected variables showed moderate accuracy for general wards in 1 English hospital. This demonstrates the potential for automating assessments of nurse staffing requirements from routine data, reducing time spent on this nonclinical overhead, and improving monitoring of real-time staffing pressures.

Keywords: nursing staff; predictive model; safer nursing care tool; staffing; workload.

Figure 1.. Distribution of whole-time equivalent (WTE) per patient values from safer nursing care tool ward assessments.

Figure 2.. Relative contribution each predictor makes to the model fit.

Figure 3.. Calibration of predictions in the test set. WTE: whole-time equivalent.

Figure 4.. Bland-Altman plot of test model assessments and predictions. pred: model prediction; wpp: whole-time equivalent per patient.