Method:
We identified 554 hospitals performing radical prostatectomy from the Healthcare Cost and Utilization Project Statewide Inpatient Databases (SID) for seven states. We used publicly available data from the website of the surgical robot’s sole manufacturer (Intuitive Surgical, Sunnyvale, CA) combined with data collected from the websites and personnel of the hospitals themselves to ascertain the timing of robot acquisition through 2008. One hundred thirty four hospitals (24%) had acquired a surgical robot by the end of 2008. We geocoded the address of each hospital and studied various neighborhood definitions based on distance to its nearest neighbor or group of neighbors. We developed and employed a two-state Markov chain method to model the acquisition process spatially and temporally. By modeling the transition probability with a logistic regression model, we quantified the “neighborhood effect” on the acquisition of the surgical robot while adjusting simultaneously for known confounders.
Result: After adjusting for hospital teaching status, surgical volume, urban status and number of hospital beds, the Markov chain analysis demonstrated that a hospital whose nearest neighbor had acquired a surgical robot has a higher likelihood of itself acquiring a surgical robot. (OR=1.71, p=0.02). Sensitivity tests using alternate definitions of hospital neighborhood yielded similar results in some cases.
Conclusion: There is a significant spatial and temporal association for hospitals acquiring surgical robots during the study period. Hospitals were more likely to acquire a surgical robot during the robot’s early adoption phase if their nearest neighbors had already done so, though the effect was modest and sensitive to the definition of neighborhood.