运用排队论分析产科手术室人员配备与手术效率。
Obstetric operating room staffing and operating efficiency using queueing theory.
机构信息
Department of Anesthesiology & Perioperative Medicine, University of Pittsburgh, 300 Halket Street #3510, Pittsburgh, PA, 15215, USA.
Department of Obstetrics & Gynecology, UPMC Magee-Womens Hospital, University of Pittsburgh, Pittsburgh, PA, USA.
出版信息
BMC Health Serv Res. 2023 Oct 25;23(1):1147. doi: 10.1186/s12913-023-10143-0.
INTRODUCTION
Strategies 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.
METHODS
Data 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: P = 1-(λ/ µ) and P = P (λ/µ) where n = number of patients. P 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.
RESULTS
There 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 P = 0.61, while the probability of 1 patient in the system was P = 0.23, and the probability of 2 or more patients in the system was P = 0.05 (P = 0.006). However, between peak hours 07:00-19:00, λ was 0.45, µ was 0.87, s was 3, P was 0.48; P was 0.25; and P was 0.07 (P = 0.01, P = 0.002, P = 0.0003).
CONCLUSION
QTA 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.
简介
已经描述了在非手术室环境中实现效率的策略，但是分娩单元中的紧急情况和竞争优先级可能使设定最佳人员配备和运营基准变得具有挑战性。本研究使用排队论分析（QTA）使用真实世界的数据来确定最佳分娩中心手术室（OR）和人员配备资源。
方法
从 2019 年 7 月至 2020 年 6 月，从所有分娩和分娩手术室活动中提取了四级产妇中心的数据（每年 9626 例分娩，剖宫产率为 32%）。QTA 有两个变量：平均到达率λ和平均服务率μ。QTA 公式计算出概率：P = 1-（λ/μ）和 P = P（λ/μ），其中 n = 患者人数。P 是给定时间内队列中没有患者的概率。使用多阶段多通道分析来深入了解最佳人员和空间利用情况，同时假设预先设定的安全参数（即，计划外剖宫产的决策时间为 30 分钟；紧急剖宫产的时间应≤5 分钟；禁食时间不应超过 8 小时）。为了达到这些安全目标，假设等待的患者比例<0.5%。
结果
手术室共有 4017 项活动，研究期间有 3092 例剖宫产。高峰时段 07:00-19:00 的到达率λ为 0.45（每小时患者数），而 24 小时的平均到达率λ为 0.34。每个 OR 团队的服务率（µ）为 0.87（每小时患者数），无论高峰时段还是 24 小时。专门从事 OR 活动的服务器团队（s）数量在 2 到 5 之间变化。在 24 小时内，系统中没有患者的概率为 P = 0.61，而系统中只有 1 名患者的概率为 P = 0.23，系统中有 2 名或更多患者的概率为 P = 0.05（P = 0.006）。但是，在高峰时段 07:00-19:00 之间，λ为 0.45，µ为 0.87，s为 3，P 为 0.48；P 为 0.25；P 为 0.07（P = 0.01，P = 0.002，P = 0.0003）。
结论
QTA 是一种有用的工具，可以在维持假定的安全标准并考虑日常活动的高峰和低谷的情况下，告知分娩中心 OR 的效率。我们的研究结果表明，QTA 可以通过改变模型参数来指导各种容量的产妇中心的人员配备。QTA 可以为个别医院级别的决策提供信息，以设置人员配备和空间要求，从而实现安全有效的围手术期产妇护理。
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