Matching Patients with Surgeons: Heterogeneous Effects of Surgical Volume on Surgery Duration
36 Pages Posted: 17 Nov 2020
Date Written: September 25, 2020
We study the heterogeneous effects of surgical volume on surgery duration and address the challenges of matching patients with surgeons to improve hospitals’ operational efficiency. Using abdominal surgery as the clinical setting, we first provide empirical evidence that the effect of surgical volume on surgery duration is heterogeneous across patients. We then apply the causal forest approach to generate patient-specific information that captures the heterogeneous volume effects for different patients. We find the effect of surgical volume varies widely across different patients. More specifically, surgical volume has a significant effect on surgery duration for 88% of patients but not for the remaining 12%. Among the patients with significant effects, the largest effect is around 10 times larger than the smallest effect. Finally, we develop an optimization model to compare the same hospital’s operational efficiency with and without patient-specific information. We find patient-specific information can reduce the total duration of surgeries by 3% to 18%, depending on the mix of patients and surgeons. The results of this study are useful to researchers and policymakers because we provide empirical evidence that the effect of surgical volume is heterogeneous, and address the challenges of estimating the heterogeneous effects for different patients. The results are also useful to hospital administrators because we show a hospital can improve its operational efficiency by using patient-specific information to match patients with surgeons.
Keywords: Causal inference, machine learning, surgeon experience, surgery scheduling
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