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Obtaining and Modeling Variability in Travel Times From Off-Site Satellite Clinics to Hospitals and Surgery Centers for Surgeons and Proceduralists Seeing Office Patients in the Morning and Performing a To-Follow List of Cases in the Afternoon

Epstein, Richard H. MD, FASA*; Dexter, Franklin MD, PhD, FASA; Smaka, Todd J. MD*

doi: 10.1213/ANE.0000000000004148
Technology, Computing and Simulation: PDF Only
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BACKGROUND: 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.

METHODS: 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.

RESULTS: 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.

CONCLUSIONS: 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.

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.

Accepted for publication February 21, 2019.

Funding: Departmental.

Conflicts of Interest: See Disclosures at the end of the article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (www.anesthesia-analgesia.org).

Implication Statement: Concerns of operating room managers at facilities providing afternoon start times to surgeons should not focus on traffic variability, which is small, but rather toward improving the accuracy of case duration predictions.

Reprints will not be available from the authors.

Address correspondence to Richard H. Epstein, MD, FASA, Department of Anesthesiology, Pain Management and Perioperative Medicine, 1400 NW 12th Ave, Suite 3075, Miami, FL 33136. Address email to repstein@med.miami.edu.

© 2019 International Anesthesia Research Society
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