A robust estimation model for surgery durations with temporal, operational, and surgery team effects

• Published in: Health care management science Vol. 18; no. 3; pp. 222 - 233
• Main Authors: Kayış, Enis, Khaniyev, Taghi T., Suermondt, Jaap, Sylvester, Karl
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2015; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Appointments and Schedules; Business and Management; Econometrics; Estimates; Estimating techniques; General Surgery; Health Administration; Health Informatics; Health Services Research; Hospitals; Humans; Industrial engineering; Management; Mathematical models; Methods; Models, Theoretical; Operating Rooms - organization & administration; Operations research; Operations Research/Decision Theory; Personnel Staffing and Scheduling - organization & administration; Quality of Health Care; Scheduling; Studies; Surgeons; Surgery; Surgery Department, Hospital - organization & administration; Time Factors; EHR data; Operating room planning; Surgical team composition and experience; Health care analytics; Surgery duration estimation
• ISSN: 1386-9620; 1572-9389
• DOI: 10.1007/s10729-014-9309-8

For effective operating room (OR) planning, surgery duration estimation is critical. Overestimation leads to underutilization of expensive hospital resources (e.g., OR time) whereas underestimation leads to overtime and high waiting times for the patients. In this paper, we consider a particular estimation method currently in use and using additional temporal, operational, and staff-related factors provide a statistical model to adjust these estimates for higher accuracy. The results show that our method increases the accuracy of the estimates, in particular by reducing large errors. For the 8093 cases we have in our data, our model decreases the mean absolute deviation of the currently used scheduled duration (42.65 ± 0.59 minutes) by 1.98 ± 0.28 minutes. For the cases with large negative errors, however, the decrease in the mean absolute deviation is 20.35 ± 0.74 minutes (with a respective increase of 0.89 ± 0.66 minutes in large positive errors). We find that not only operational and temporal factors, but also medical staff and team experience related factors (such as number of nurses and the frequency of the medical team working together) could be used to improve the currently used estimates. Finally, we conclude that one could further improve these predictions by combining our model with other good prediction models proposed in the literature. Specifically, one could decrease the mean absolute deviation of 39.98 ± 0.58 minutes obtained via the method of Dexter et al (Anesth Analg 117(1):204–209, 2013 ) by 1.02 ± 0.21 minutes by combining our method with theirs.