Robot-assisted surgery is a major advancement in minimally invasive surgical care delivery. It allows surgeons to gain additional dexterity, improved precision in surgical tasks, enhanced three-dimensional vision of the surgical field, and superior accessibility to the surgical field. Notwithstanding the clinical outcome benefits (such as reduced blood loss and surgical duration, and shorter length-of-stay at a hospital), the cost of performing robot-assisted surgery is significantly high, due to the investments needed for robot acquisition, maintenance, and procurement of surgical accessories. In this study, we address the twin objectives of a hospital where robot-assisted surgery is performed: maximize the clinical outcome benefits and minimize the total cost of robot-assisted surgery. The surgical robot that is the focus of the study is a da Vinci surgical system. The surgical procedure that serves as the context of this study is hysterectomy. We demonstrate the application of an integrated methodological approach—combining empirical analysis involving an on-site, prospective investigation at a hospital and retrospective analysis of archival data from the hospital for robustness checks, analytical modeling, and discrete event simulation—to identify and analyze actionable policies that a hospital can implement. The specific policies we analyze are related to: (a) patient triaging for robot-assisted surgery based on the criticality of disease condition of a patient; (b) the optimal size of surgeon pool to facilitate the development of surgeon experience and learning; and (c) the minimum experience level of a surgeon with a robot needed to be included in the surgeon pool for robot-assisted surgery. The key contributions of this article are in demonstrating the following: First, hospital-level policies can help to realize both the clinical outcome and cost benefits of a surgical robot. Second, the criticality of patient condition is a significant determinant of surgeon learning in robot-assisted surgery. Third, application of the proposed integrated methodological approach can yield nuanced and actionable insights into an operational setting where data availability is limited and generalizability of study insights is a concern, as is the case in this study.
Bibliographical noteFunding Information:
The authors express their gratitude to the partner hospital for access, encouragement and support in conducting this study. The authors are indebted to Scott Bosch, Shoubhik Sinha and Gagan Sharma for their guidance and assistance in data collection. The authors are grateful to the Special Issue Guest Editor, Lawrence Fredendall, an anonymous associate editor and two anonymous reviewers for their guidance and detailed comments on earlier versions of the paper.
© 2019 Association for Supply Chain Management, Inc.
- cost of healthcare
- hospital policy
- multi-method study
- robot-assisted surgery
- surgeon learning