Accounting for patient heterogeneity in nurse staffing using a queueing-theory approach

Evidence from observational studies suggests that inadequate nurse staffing in hospitals and heavy nurse workload may compromise patient safety and quality of care. There are recommended minimum nurse-to-patient ratios for different types of inpatient care settings. However, nursing-care intensity may vary across different patients within an inpatient unit depending on the severity of their medical condition, potentially rendering fixed nurse-to-patient ratios ineffective. This study aims at developing nurse-staffing strategies that explicitly account for patient heterogeneity. Using queueing theory, we develop a stochastic framework to model direct nursing care provided in inpatient-care units. The stochastic model is then used to measure different performance metrics that evaluate the efficiency and timeliness of inpatient-care delivery. The trade-off between those performance metrics and the nursing staff level is quantified, which can assist clinicians with determining minimum nursing staff levels that ensure timely delivery of nursing care to a given patient mix.