Meredith Paul, Saville Christina, Dall'Ora Chiara, Weeks Tom, Wierzbicki Sue, Griffiths Peter
School of Health Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Building 67, University Road, Southampton, S017 1BJ, United Kingdom, 44 23 8059 5903, 44 23 8059 8909.
Workforce & Health Systems, National Institute for Health Research Applied Research Collaboration (Wessex), Southampton, United Kingdom.
JMIR Med Inform. 2025 Jul 31;13:e71666. doi: 10.2196/71666.
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.
The objective of this study is to explore whether an algorithm could estimate ward workload using existing routinely recorded data.
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.
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.
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.
由于基于病房占用率、患者病情严重程度和护理依赖程度的需求波动，管理护士人员配置较为复杂。实时监测人员配置充足情况有助于实现安全、高效的人员调配。患者分类系统（PCSs）被用于每班工作量测量，但这给病房护理人员增加了一项频繁的行政任务。
本研究的目的是探讨一种算法能否使用现有的常规记录数据来估计病房工作量。
使用支持更安全护理工具的患者分类系统的匿名入院记录和评估数据，以确定2017年2月至2020年2月期间英国一家医院内科和外科病房的护理需求。记录按病房和时间进行关联。数据被分为训练集（75%）和测试集（25%）。我们使用常规记录的行政数据和入院时的国家早期预警评分建立了病房工作量预测模型（以患者分类系统测量）。结果变量是源自患者分类的病房工作量，以每名患者的全时等效（WTE）护理人员数量衡量。
在来自42个病房的11592次病房评估测试集中，每名患者的平均WTE为1.64，该模型的平均绝对误差为0.078，平均百分比误差为4.9%。预测值与评估值差异的Bland-Altman图显示，95%的差异在每名患者0.21 WTE以内。
从相对少量的常规收集变量对护理工作量进行的预测在一家英国医院的普通病房中显示出中等准确性。这表明从常规数据自动评估护士人员配置需求具有潜力，可减少在这项非临床管理工作上花费的时间，并改善对实时人员配置压力的监测。
