Enis Kayis

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

AbstractFor 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.

A note on the can-order policy for the two-item stochastic joint-replenishment problem

In this paper, a two-item continuous-review inventory system is studied. Demands for item 1 and item 2 occur at epochs generated by independent Poisson processes. In addition to the standard cost structure, there is economy of scale in joint replenishment. For the continuous joint replenishment problem, the literature proposes the can-order policy. Under this policy, an order is triggered by item 1 at its demand epoch, when its inventory position falls to its reorder level. In this situation, if the inventory position of item 2 is at or below its “can-order” level, item 2 is also included in this order and a discounted fixed ordering cost is charged for it. As a result, the inventory positions of both items are raised to their respective order-up-to levels. Reciprocally, the same procedure is valid at the demand epoch of item 2. In this study, this two-item inventory system is modeled as a semi-Markov decision process and a simple enumeration algorithm is proposed for its solution. We show that previous formulations of the problem do not necessarily converge to the best can-order policy by providing numerical examples.