Key result
Discrete event simulation coupled with a queuing model demonstrated that established time-varying nurse-to-patient ratios systematically underestimated nurse staffing needs by up to 20%.
Why the study?
Does discrete event simulation coupled with a queuing model improve nurse staffing level estimation compared to NPR-based methods in perianesthesia care units?
Does discrete event simulation coupled with a queuing model improve nurse staffing level estimation compared to NPR-based methods in perianesthesia care units?
Incorporating a queuing model within a discrete event simulation yields a more accurate estimation of nurse staffing levels in perianesthesia care units, avoiding the up to 20% underestimation seen with traditional NPR methods.
May warrant caution using traditional NPR for perianesthesia staffing; leaves open whether DES models improve outcomes in practice.
High variability in patient flow and changing patient acuity in perianesthesia care units (preparation/postanesthesia recovery [PREP/PACU]) is a challenge to efficient management of nurse staffing. Common approaches to estimate required nurse staffing levels that use PACU patient census over time, multiplied by nurse to patient ratios (NPR), may systematically underestimate nurse staffing needs. The objective of this study is to use discrete event simulation (DES) coupled with a queuing model to project nurse staffing levels and account for the dynamics of assigning nurses to patients. We evaluated the reference timevarying NPR-based method, which takes into account changing patient acuities over time, and showed that the current reference methods underestimate staffing requirements. These data parameterized the DES, which modeled the temporal patterns of weekly perioperative patient flow and mimicked the nurse protocol to manage stochastically simulated patients for a PREP/PACU within an urban 1059-bed medical center. Efficient nurse staffing level estimates over time were the primary outputs computed by the DES. Previously established time-varying (based on acuity) NPR systematically underestimated (up to 20%) nurse staffing needs, given common nurse-to-patient assignment protocols. We show that incorporating a queuing model within a DES will yield a proper estimation of staffing levels.
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Siddiqui et al. (2017) studied Perianesthesia care unit patient flow. Discrete event simulation (DES) coupled with a queuing model vs. Time-varying nurse to patient ratio (NPR)-based method was evaluated on Efficient nurse staffing level estimates over time. Discrete event simulation coupled with a queuing model demonstrated that established time-varying nurse-to-patient ratios systematically underestimated nurse staffing needs by up to 20%.
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