From the Department of Anesthesiology, Pain Management and Perioperative Medicine, University of Miami, Miami, Florida.
Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, Iowa.
Anesth Analg. 2020 Jul;131(1):228-238. doi: 10.1213/ANE.0000000000004148.
Hospitals achieve growth in surgical caseload primarily from the additive contribution of many surgeons with low caseloads. Such surgeons often see clinic patients in the morning then travel to a facility to do 1 or 2 scheduled afternoon cases. Uncertainty in travel time is a factor that might need to be considered when scheduling the cases of to-follow surgeons. However, this has not been studied. We evaluated variability in travel times within a city with high traffic density.
We used the Google Distance Matrix application programming interface to prospectively determine driving times incorporating current traffic conditions at 5-minute intervals between 9:00 AM and 4:55 PM during the first 4 months of 2018 between 4 pairs of clinics and hospitals in the University of Miami health system. Travel time distributions were modeled using lognormal and Burr distributions and compared using the absolute and signed differences for the median and the 0.9 quantile. Differences were evaluated using 2-sided, 1-group t tests and Wilcoxon signed-rank tests. We considered 5-minute signed differences between the distributions as managerially relevant.
For the 80 studied combinations of origin-to-destination pairs (N = 4), day of week (N = 5), and the hour of departure between 10:00 AM and 1:55 PM (N = 4), the maximum difference between the median and 0.9 quantile travel time was 8.1 minutes. This contrasts with the previously published corresponding difference between the median and the 0.9 quantile of 74 minutes for case duration. Travel times were well fit by Burr and lognormal distributions (all 160 differences of medians and of 0.9 quantiles <5 minutes; P < .001). For each of the 4 origin-destination pairs, travel times at 12:00 PM were a reasonable approximation to travel times between the hours of 10:00 AM and 1:55 PM during all weekdays.
During mid-day, when surgeons likely would travel between a clinic and an operating room facility, travel time variability is small compared to case duration prediction variability. Thus, afternoon operating room scheduling should not be restricted because of concern related to unpredictable travel times by surgeons. Providing operating room managers and surgeons with estimated travel times sufficient to allow for a timely arrival on 90% of days may facilitate the scheduling of additional afternoon cases especially at ambulatory facilities with substantial underutilized time.
医院的手术量增长主要来自于低手术量的多位外科医生的累加贡献。这些外科医生通常在上午看诊患者，然后前往医院进行 1 到 2 个下午的预约手术。当安排后续外科医生的手术时，需要考虑到行程时间的不确定性因素。然而，目前尚未对此进行研究。我们评估了在交通繁忙的城市中行程时间的可变性。
我们使用 Google 距离矩阵应用程序编程接口（API），前瞻性地确定了在 2018 年 4 月的前 4 个月中，上午 9 点至下午 4 点 55 分之间每 5 分钟一次的行程时间，包括当前交通状况。使用对数正态和 Burr 分布对行程时间分布进行建模，并通过中位数和 0.9 分位数的绝对差值和符号差值进行比较。使用双侧、单组 t 检验和 Wilcoxon 符号秩检验评估差异。我们认为分布之间 5 分钟的符号差异在管理上是相关的。
对于 80 种研究的起点-终点对组合（N=4）、一周中的天数（N=5）以及上午 10 点至下午 1 点 55 分之间的出发小时（N=4），中位数和 0.9 分位数的最大行程时间差异为 8.1 分钟。这与之前发表的手术持续时间中位数和 0.9 分位数之间的差异为 74 分钟形成鲜明对比。行程时间很好地符合 Burr 和对数正态分布（中位数和 0.9 分位数的 160 个差异均<5 分钟；P<.001）。对于 4 个起点-终点对中的每一个，下午 12 点的行程时间可以很好地近似于所有工作日上午 10 点至下午 1 点 55 分之间的行程时间。
在中午时分，当外科医生可能在诊所和手术室之间往返时，与手术持续时间预测的可变性相比，行程时间的可变性较小。因此，不应因为担心外科医生的行程时间不可预测而限制下午的手术室安排。为手术室经理和外科医生提供足够的估计行程时间，以便他们在 90%的时间内及时到达，可以促进下午额外手术的安排，特别是在利用时间不足的门诊设施。
