JMIR Publications

ABSTRACT

Background:

Variations in patient demand increase the challenge of balancing high-quality nursing skill mixes against budgetary constraints. Developing staffing guidelines that will allow high quality care at minimal cost requires first exploring the dynamic changes in nursing workload over the course of a day.

Objective:

Accordingly, this longitudinal study analyzed nursing care supply and demand in 30-minute increments over a period of three years. We assessed five care factors: 1) patient count (care demand); 2) nurse count (care supply); 3) patient-to-nurse ratio for each nurse group; 4) extreme supply-demand mismatches; and 5) patient turnover, i.e., number of admissions, discharges, and transfers.

Methods:

Our retrospective analysis of data from the Inselspital University Hospital Bern, Switzerland included all inpatients and nurses working on their units from 1 January 2015 to 31 December 2017. Two data sources were used: 1) The nurse staffing system (tacs®) (including information about nurses and all care they provided to patients, alongside their working time and admission, discharge, and transfer dates/times); and 2) medical discharge data (including patient demographics, further admission/discharge details, and diagnoses). Based on several identifiers, these two data sources were linked.

Results:

Our final dataset included more than 58 million data points on 128,484 patients and 4,633 nurses across 70 units. Compared with patient turnover, fluctuations in the number of nurses were less pronounced: differences mainly coincided with shifts (night, morning, afternoon). While the percentage of shifts with extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), the percentage lying within “normal” ranges ranged from fewer than 50% to more than 80%. Patient turnover occurred throughout the measurement period, but was lowest at night.

Conclusions:

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 through the day, is the key driver of patient-to-nurse ratio changes. This demand-side variability challenges the supply-side mandate to provide safe and reliable care. Detecting and describing variability patterns 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.