A Multi-Treatment Forest Approach for Analyzing the Heterogeneous Effects of Team Familiarity 

M. Zhang, G. Wang, W. Hopp, and M. Mathis. Under Major Revision.

Extensive research has revealed that prior collaborative experiences among team members (called "team familiarity") enhance outcomes of group work in many different environments. In this study, we examine the effect of team familiarity on surgery duration and extend the literature on team dynamics by examining whether the effect of team familiarity is heterogeneous across patients. Because we use multiple variables to measure team familiarity (i.e., multiple treatments of interest), we first develop a new approach, which we call the "MT forest" approach, to estimate heterogeneous effects of multiple treatments and demonstrate the effectiveness of this approach using synthetic data. Then, we apply the MT forest approach to an orthopedic surgery setting to estimate the heterogeneous effects of team familiarity on surgery duration, and investigate how the effect varies across patient features. We find (1) an increase in team familiarity score, especially the anesthesiologist-nurse and surgeon-anesthesiologist familiarity scores, significantly reduces surgery duration, and (2) the effect of team familiarity is heterogeneous across patients with different features. Finally, we develop an optimization model to assess the value of leveraging the heterogeneous effects of team familiarity to better match surgical teams with patients. This research contributes to the academic literature by providing a new approach to estimating heterogeneous effects of multiple treatments and by providing empirical evidence that the effect of team familiarity is heterogeneous across patients. Our results are also of potential value to healthcare providers because they imply that leveraging the heterogeneous effects of team familiarity to better match surgical teams with patients can improve hospital operational efficiency.