Musy Sarah N, Endrich Olga, Leichtle Alexander B, Griffiths Peter, Nakas Christos T, Simon Michael
Institute of Nursing Science, University of Basel, Basel, Switzerland.
Nursing and Midwifery Research Unit, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
J Med Internet Res. 2020 Apr 2;22(4):e15554. doi: 10.2196/15554.
Variations in patient demand increase the challenge of balancing high-quality nursing skill mixes against budgetary constraints. Developing staffing guidelines that allow high-quality care at minimal cost requires first exploring the dynamic changes in nursing workload over the course of a day.
Accordingly, this longitudinal study analyzed nursing care supply and demand in 30-minute increments over a period of 3 years. We assessed 5 care factors: patient count (care demand), nurse count (care supply), the patient-to-nurse ratio for each nurse group, extreme supply-demand mismatches, and patient turnover (ie, number of admissions, discharges, and transfers).
Our retrospective analysis of data from the Inselspital University Hospital Bern, Switzerland included all inpatients and nurses working in their units from January 1, 2015 to December 31, 2017. Two data sources were used. The nurse staffing system (tacs) provided information about nurses and all the care they provided to patients, their working time, and admission, discharge, and transfer dates and times. The medical discharge data included patient demographics, further admission and discharge details, and diagnoses. Based on several identifiers, these two data sources were linked.
Our final dataset included more than 58 million data points for 128,484 patients and 4633 nurses across 70 units. Compared with patient turnover, fluctuations in the number of nurses were less pronounced. The differences mainly coincided with shifts (night, morning, evening). While the percentage of shifts with extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), the percentage within "normal" ranges ranged from fewer than 50% to more than 80%. Patient turnover occurred throughout the measurement period but was lowest at night.
Based on measurements of patient-to-nurse ratio and patient turnover at 30-minute intervals, our findings indicate that the patient count, which varies considerably throughout the day, is the key driver of changes in the patient-to-nurse ratio. This demand-side variability challenges the supply-side mandate to provide safe and reliable care. Detecting and describing patterns in variability such as these are key to appropriate staffing planning. This descriptive analysis was a first step towards identifying time-related variables to be considered for a predictive nurse staffing model.
患者需求的变化增加了在高质量护理技能组合与预算限制之间取得平衡的挑战。制定能够以最低成本提供高质量护理的人员配置指南，首先需要探索一天中护理工作量的动态变化。
因此，这项纵向研究以30分钟为增量，分析了3年期间的护理供需情况。我们评估了5个护理因素：患者数量（护理需求）、护士数量（护理供给）、每个护士群体的患者与护士比例、极端供需不匹配情况以及患者周转情况（即入院、出院和转院人数）。
我们对瑞士伯尔尼大学医院因塞尔医院2015年1月1日至2017年12月31日期间所有住院患者和各科室护士的数据进行回顾性分析。使用了两个数据源。护士人员配置系统（tacs）提供了有关护士及其为患者提供的所有护理、工作时间以及入院、出院和转院日期及时间的信息。医疗出院数据包括患者人口统计学信息、进一步的入院和出院细节以及诊断信息。基于几个标识符，将这两个数据源进行了关联。
我们的最终数据集包含了70个科室中128484名患者和4633名护士的超过5800万个数据点。与患者周转情况相比，护士数量的波动不太明显。差异主要与班次（夜间、上午、晚上）一致。虽然极端人员配置波动班次的百分比从不到3%（上午）到30%（晚上和夜间）不等，但“正常”范围内的百分比从不到50%到超过80%不等。患者周转在整个测量期间都有发生，但夜间最低。
基于每隔30分钟对患者与护士比例和患者周转情况的测量，我们的研究结果表明，一天中变化很大的患者数量是患者与护士比例变化的关键驱动因素。这种需求侧的可变性对供应侧提供安全可靠护理的要求构成了挑战。检测和描述诸如此类的可变性模式是进行适当人员配置规划的关键。这种描述性分析是朝着确定预测护士人员配置模型时要考虑的与时间相关变量迈出的第一步。
