From the Department of Anesthesia, Perioperative Medicine & Pain Management, University of Miami, Miller School of Medicine, Miami, Florida.
Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, Iowa.
Anesth Analg. 2019 Nov;129(5):1265-1272. doi: 10.1213/ANE.0000000000003320.
For emergent procedures, in-house teams are required for immediate patient care. However, for many procedures, there is time to bring in a call team from home without increasing patient morbidity. Anesthesia providers taking subspecialty or backup call from home are required to return to the hospital within a designated number of minutes. Driving times to the hospital during the hours of call need to be considered when deciding where to live or to visit during such calls. Distance alone is an insufficient criterion because of variable traffic congestion and differences in highway access. We desired to develop a simple, inexpensive method to determine postal codes surrounding hospitals allowing a timely return during the hours of standby call.
Pessimistic travel times and driving distances were calculated using the Google distance matrix application programming interface for all N = 136 postal codes within 60 great circle ("straight line") miles of the University of Miami Hospital (Miami, FL) during all 108 weekly standby call hours. A postal code was acceptable if the estimated longest driving time to return to the hospital was ≤60 minutes (the anesthesia department's service commitment to start an urgent case during standby call). Linear regression (with intercept = 0) minimizing the mean absolute percentage difference between the distances (great circle and driving) and the pessimistic driving times to return to the hospital was performed among all 136 postal codes. Implementation software written in Python is provided.
Postal codes allowing return to the studied hospital within the specified interval were identified. The linear regression showed that driving distances correlated poorly with the longest driving time to return to the hospital among the 108 weekly call hours (mean absolute percentage error = 25.1% ± 1.7% standard error [SE]; N = 136 postal codes). Great circle distances also correlated poorly (mean absolute percentage error = 28.3% ± 1.9% SE; N = 136). Generalizability of the method was determined by successful application to a different hospital in a rural state (University of Iowa Hospital).
The described method allows identification of postal codes surrounding a hospital in which personnel taking standby call could be located and be able to return to the hospital during call hours on every day of the week within any specified amount of time. For areas at the perimeter of the acceptability, online distance mapping applications can be used to check driving times during the hours of standby call.
对于紧急手术，需要有内部团队立即为患者提供护理。然而，对于许多手术，有时间从家里召集一个呼叫团队，而不会增加患者的发病率。从家里接受专科或候补呼叫的麻醉师需要在指定的分钟数内返回医院。在决定在这些呼叫期间住在哪里或去哪里时，需要考虑在呼叫期间开车到医院的时间。仅距离是不够的标准，因为交通拥堵和高速公路通行的差异。我们希望开发一种简单、廉价的方法来确定医院周围的邮政编码，以便在候补呼叫的时间内及时返回。
使用 Google 距离矩阵应用程序编程接口，为距离迈阿密大学医院（迈阿密，佛罗里达州） 60 大圆弧（“直线”）英里范围内的所有 N = 136 个邮政编码计算所有 108 个每周备用呼叫小时的悲观旅行时间和驾驶距离。如果返回医院的估计最长驾驶时间≤60 分钟（麻醉部门在备用呼叫期间开始紧急手术的服务承诺），则邮政编码是可接受的。在所有 136 个邮政编码中进行了最小化返回医院的距离（大圆弧和驾驶）和悲观驾驶时间之间的平均绝对百分比差异的线性回归（截距= 0）。
确定了可在指定时间内返回研究医院的邮政编码。线性回归表明，在 108 个每周呼叫小时中，驾驶距离与返回医院的最长驾驶时间相关性较差（136 个邮政编码的平均绝对百分比误差= 25.1% ± 1.7%标准误差[SE]）。大圆弧距离也相关性较差（136 个邮政编码的平均绝对百分比误差= 28.3% ± 1.9% SE）。该方法的通用性通过在农村州的另一家医院（爱荷华大学医院）的成功应用得到了确定。
所描述的方法允许识别在医院周围的邮政编码，以便在呼叫期间可以在每周的每一天的任何指定时间内返回医院的接受范围内的人员。对于可接受性的外围区域，可以使用在线距离映射应用程序检查在备用呼叫期间的驾驶时间。
