TY - JOUR
T1 - Pattern-based strategic surgical capacity allocation
AU - Bai, Miao
AU - Pasupathy, Kalyan S.
AU - Sir, Mustafa Y.
N1 - Funding Information:
This work is funded in part by the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery .
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/6
Y1 - 2019/6
N2 - Strategic allocation of limited operating room (OR) capacity to surgeons is crucial for the coordination of surgical work flow, including planning of consultation and surgery days, and staff assignment to perioperative teams. However, it is a challenging problem in practice, since the capacity allocation needs to be cyclic for schedule predictability and surgical team coordination, and also needs to satisfy surgeons’ preferences. It is further complicated by the practice of surgeons sharing ORs. In this study, we propose a mathematical optimization model to coordinate capacity allocation among surgeons in order to improve the utilization of surgical capacity. We introduce the concept of capacity allocation patterns to account for schedule cyclicity and surgeons’ preferences. Further, we develop a data-driven approach to coordinate OR sharing among surgeons based on their historical OR usage. The proposed methodology is applied to a case study with data from a surgical division at Mayo Clinic. Compared with the state-of-the-practice, the proposed approach shows a substantial potential in reducing the maximum number of ORs allocated daily to the division with little overtime. With a solution time of less than 0.5 s, the proposed methodology can be readily used as a decision support tool in surgical practice.
AB - Strategic allocation of limited operating room (OR) capacity to surgeons is crucial for the coordination of surgical work flow, including planning of consultation and surgery days, and staff assignment to perioperative teams. However, it is a challenging problem in practice, since the capacity allocation needs to be cyclic for schedule predictability and surgical team coordination, and also needs to satisfy surgeons’ preferences. It is further complicated by the practice of surgeons sharing ORs. In this study, we propose a mathematical optimization model to coordinate capacity allocation among surgeons in order to improve the utilization of surgical capacity. We introduce the concept of capacity allocation patterns to account for schedule cyclicity and surgeons’ preferences. Further, we develop a data-driven approach to coordinate OR sharing among surgeons based on their historical OR usage. The proposed methodology is applied to a case study with data from a surgical division at Mayo Clinic. Compared with the state-of-the-practice, the proposed approach shows a substantial potential in reducing the maximum number of ORs allocated daily to the division with little overtime. With a solution time of less than 0.5 s, the proposed methodology can be readily used as a decision support tool in surgical practice.
KW - Data-driven
KW - Mathematical optimization
KW - Pattern-based
KW - Surgery capacity allocation
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U2 - 10.1016/j.jbi.2019.103170
DO - 10.1016/j.jbi.2019.103170
M3 - Article
C2 - 30959205
AN - SCOPUS:85064587971
SN - 1532-0464
VL - 94
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
M1 - 103170
ER -