Huilin Li1, Mitchell H Gail2, R Scott Braithwaite3, Heather T Gold4, Dawn Walter5, Mengling Liu1, Cary P Gross6, Danil V Makarov7. 1. Department of Population Health, New York University, New York, NY, 10016 ; New York University Cancer Institute. 2. Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, NIH, Rockville, MD, 20852-7242. 3. Department of Population Health, New York University, New York, NY, 10016. 4. Department of Population Health, New York University, New York, NY, 10016 ; New York University Cancer Institute ; Robert F. Wagner Graduate School of Public Service. 5. Department of Urology, New York University School of Medicine. 6. Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, and Division of General Internal Medicine, Yale University School of Medicine ; United States Department of Veterans Affairs. 7. Department of Population Health, New York University, New York, NY, 10016 ; New York University Cancer Institute ; Robert F. Wagner Graduate School of Public Service ; Department of Urology, New York University School of Medicine ; United States Department of Veterans Affairs.
Abstract
BACKGROUND: The surgical robot has been widely adopted in the United States in spite of its high cost and controversy surrounding its benefit. Some have suggested that a "medical arms race" influences technology adoption. We wanted to determine whether a hospital would acquire a surgical robot if its nearest neighboring hospital already owned one. METHODS: We identified 554 hospitals performing radical prostatectomy from the Healthcare Cost and Utilization Project Statewide Inpatient Databases 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 hospitals to ascertain the timing of robot acquisition during year 2001 to 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 determined a hospital's likelihood to acquire a surgical robot based on whether its nearest neighbor owned a surgical robot. We developed a Markov chain method to model the acquisition process spatially and temporally and quantified the "neighborhood effect" on the acquisition of the surgical robot while adjusting simultaneously for known confounders. RESULTS: 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 had a higher likelihood itself acquiring a surgical robot. (OR=1.71, 95% CI: 1.07-2.72, p=0.02). 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 neighbor had already done so.
BACKGROUND: The surgical robot has been widely adopted in the United States in spite of its high cost and controversy surrounding its benefit. Some have suggested that a "medical arms race" influences technology adoption. We wanted to determine whether a hospital would acquire a surgical robot if its nearest neighboring hospital already owned one. METHODS: We identified 554 hospitals performing radical prostatectomy from the Healthcare Cost and Utilization Project Statewide Inpatient Databases 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 hospitals to ascertain the timing of robot acquisition during year 2001 to 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 determined a hospital's likelihood to acquire a surgical robot based on whether its nearest neighbor owned a surgical robot. We developed a Markov chain method to model the acquisition process spatially and temporally and quantified the "neighborhood effect" on the acquisition of the surgical robot while adjusting simultaneously for known confounders. RESULTS: 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 had a higher likelihood itself acquiring a surgical robot. (OR=1.71, 95% CI: 1.07-2.72, p=0.02). 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 neighbor had already done so.
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