Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
J Nurs Manag. 2021 Oct;29(7):2278-2287. doi: 10.1111/jonm.13346. Epub 2021 Aug 3.
To identify, simulate and evaluate the formal and informal patient-level and unit-level factors that nurse managers use to determine the number of nurses for each shift.
Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last-minute adjustments or inappropriate staffing levels.
Staffing schedules at a paediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modelled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels.
Nurse-to-patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift.
Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand.
Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.
确定、模拟和评估护士管理者用于确定每班护士人数的正式和非正式的患者层面和单元层面的因素。
护士人员配置计划通常基于午夜普查等指标制定，这些指标没有考虑到季节性或中午人员流动情况，导致最后一刻的调整或人员配置水平不当。
根据护理管理访谈中护士与患者分配规则对儿科重症监护病房（PICU）的人员配置计划进行模拟。多元回归模型分析了计划和历史人员配置水平之间的差异，并构建了减少这些差异的规则。主要结果是模拟和历史人员配置水平之间的中位数差异。
护士与患者的比例每班次低估了 1.5 名护士的配置。多元回归分析确定患者周转率是造成这种差异的主要因素，亚组分析显示患者年龄和体重也很重要。新规则将差异缩小至每班次中位数 0.07 名护士。
可衡量、可预测的患者病情严重程度和历史趋势指标可能使需求与排班更好地匹配。
数据驱动的方法可以量化驱动单元需求的因素，并生成需要更少最后一刻调整的护士排班。
