Nurse staffing and skill-mix optimization in inpatient settings using a stochastic modeling approach

The World Health Organization has estimated a worldwide shortage of around nine million nurses and midwives by 2030. In the United States, a shortage of registered nurses (RN)s is predicted to occur in several states by 2030. There is a gowning concern that this shortage may adversely impact the quality of nursing care and may lead to nurse burnout. To address this concern, hospitals have adopted several staffing strategies to effectively manage their scarce nursing resources. Traditionally, nurse-to-patient ratios have been used to staff inpatient care units, which specify the number of patients that can be safely supervised by a nurse. However, patients often require different levels of care based on the complexity of their medical conditions. Furthermore, not all care tasks need the support of highly trained RNs and thus, hospitals often employ nursing teams consisting of care givers at different skill levels for cost-saving purposes. The heterogeneity in patient mix and nursing skill mix can potentially render ratio-based staffing strategies ineffective. We propose to incorporate this heterogeneity into staffing decisions using stochastic modeling approaches. In particular, queueing theory and discrete-event simulation techniques are employed to investigate the potential impacts of patient acuity and staff heterogeneity on staffing needs of inpatient units. Moreover, the developed stochastic models are embedded in a multi-criteria optimization (MCO) framework to determine the optimal number of nursing teams and the corresponding staff configurations that yield the desired trade-off between different performance evaluation criteria measuring staffing cost, timely delivery of care, and nurse burnout. The proposed models can provide decision makers and staff planners with planning tools for safe and cost-effective staffing of inpatient care units.

Author

• Eimanzadeh, Parisa

Identifier

hdl:10057/15538

OAI identifier oai:identifier

oai:soar.wichita.edu:10057/15538