Adoption of Robot-Assisted Partial Nephrectomies: A Population-Based Analysis of U.S. Surgeons from 2004 to 2013.

INTRODUCTION

Urological surgeries have contributed to the increasing prevalence of minimally invasive robotic procedures. Although factors influencing the adoption of robot-assisted radical prostatectomy have previously been identified, the explanation for the rapid rise in robotic partial nephrectomies remains unknown. Using a retrospective population-based sample, we attempt to determine hospital and surgeon-specific factors influencing a surgeon's decision to utilize robotic assistance for partial nephrectomies.

MATERIALS AND METHODS

A nationally representative weighted sample of all men who underwent a partial nephrectomy in the United States between 2003 and 2014 was identified within the Premier Hospital Database. Hospital, surgeon, and patient characteristics for each operation were analyzed. Descriptive statistics and a multivariate regression model stratified according to the Law of Diffusion of Innovation were performed.

RESULTS

A weighted sample of 14,890 nephrectomies was included in the study. Patient demographics were similar between the two groups. The adoption of robotic technology followed the Law of Diffusion of Innovation with the percentage of partial nephrectomies with robotic assistance increasing yearly, reaching 64.1% by 2013. Surgical volume was a significant factor driving the use of robotic assistance, with high volume surgeons (>5 partial nephrectomies/year) performing 23.2% more robotic partial nephrectomies per year than their low volume colleagues (< = 5 partial nephrectomies/year) from 2009 to 2013 (p < 0.001).

CONCLUSIONS

This retrospective population-based study examines key factors influencing the diffusion of robotic technology for partial nephrectomies. Surgical volume and year of surgery were found to be the most significant factor in robotic adoption, with other patient and hospital-specific characteristics playing a minor role. Future studies are needed to correlate adoption rates with the clinical or cost-effectiveness of novel technologies within the medical field to determine whether rapid adoption is a patient-centered vs a clinician-centered decision point.