Determination of Geolocations for Anesthesia Specialty Coverage and Standby Call Allowing Return to the Hospital Within a Specified Amount of Time

Abstract

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.