Obstetric operating room staffing and operating efficiency using queueing theory

BMC Health Services Research

Research

Mark Zakowski¹  Grace Lim²  Brendan Carvalho³  Beth Quinn⁴  Grant C. Lynde⁵  Annamarie J. Lim⁶ 

[1] Cedars-Sinai Medical Center, Los Angeles, CA, USA;Department of Anesthesiology & Perioperative Medicine, University of Pittsburgh, 300 Halket Street #3510, 15215, Pittsburgh, PA, USA;Department of Obstetrics & Gynecology, UPMC Magee-Womens Hospital, University of Pittsburgh, Pittsburgh, PA, USA;Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA;Department of Obstetrics & Gynecology, UPMC Magee-Womens Hospital, University of Pittsburgh, Pittsburgh, PA, USA;Hospital Corporation of America (HCA) Healthcare, Nashville, TN, USA;Schumacher Clinical Partners (SCP) Health, Traverse City, MI, USA;

关键词: Staffing;    Obstetric;    Anesthesia;    Efficiency;    Queueing;    Operating room;   

DOI  :  10.1186/s12913-023-10143-0

 received in 2023-01-15, accepted in 2023-10-13,  发布年份 2023

来源: Springer

【 摘 要 】

IntroductionStrategies to achieve efficiency in non-operating room locations have been described, but emergencies and competing priorities in a birth unit can make setting optimal staffing and operation benchmarks challenging. This study used Queuing Theory Analysis (QTA) to identify optimal birth center operating room (OR) and staffing resources using real-world data.MethodsData from a Level 4 Maternity Center (9,626 births/year, cesarean delivery (CD) rate 32%) were abstracted for all labor and delivery operating room activity from July 2019—June 2020. QTA has two variables: Mean Arrival Rate, λ and Mean Service Rate µ. QTA formulas computed probabilities: P0 = 1-(λ/ µ) and Pn = P0 (λ/µ)n where n = number of patients. P0…n is the probability there are zero patients in the queue at a given time. Multiphase multichannel analysis was used to gain insights on optimal staff and space utilization assuming a priori safety parameters (i.e., 30 min decision to incision in unscheduled CD; ≤ 5 min for emergent CD; no greater than 8 h for nil per os time). To achieve these safety targets, a < 0.5% probability that a patient would need to wait was assumed.ResultsThere were 4,017 total activities in the operating room and 3,092 CD in the study period. Arrival rate λ was 0.45 (patients per hour) at peak hours 07:00–19:00 while λ was 0.34 over all 24 h. The service rate per OR team (µ) was 0.87 (patients per hour) regardless of peak or overall hours. The number of server teams (s) dedicated to OR activity was varied between two and five. Over 24 h, the probability of no patients in the system was P0 = 0.61, while the probability of 1 patient in the system was P1 = 0.23, and the probability of 2 or more patients in the system was P≥2 = 0.05 (P3 = 0.006). However, between peak hours 07:00–19:00, λ was 0.45, µ was 0.87, s was 3, P0 was 0.48; P1 was 0.25; and P≥2 was 0.07 (P3 = 0.01, P4 = 0.002, P5 = 0.0003).ConclusionQTA is a useful tool to inform birth center OR efficiency while upholding assumed safety standards and factoring peaks and troughs of daily activity. Our findings suggest QTA is feasible to guide staffing for maternity centers of all volumes through varying model parameters. QTA can inform individual hospital-level decisions in setting staffing and space requirements to achieve safe and efficient maternity perioperative care.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

Fig. 6

12936_2017_2075_Article_IEq66.gif

Fig. 3