Outside the operating room: How a robotics program changed resource utilization on the inpatient Ward

Surgery Advances in Gynecologic Tumors: The Evolution and Outcomes of Robotic Surgery for Gynecologic Cancers in a Tertiary Center

The integration of innovation into routine clinical practice is faced with many challenges. In 2007, we received the mandate to evaluate how the introduction of a robotic program in gynecologic oncology affected patient-centered care by studying its impact on clinical outcomes and hospital resource utilization. Here we summarize the history and experience of developing a robotic surgery program for gynecologic cancers over 16 years. Analysis of the data indicates that robotic surgery improved perioperative patient clinical parameters, decreased blood loss, complications, and hospital stay, maintained the oncologic outcome, and is cost-effective, resulting in it becoming the dominant surgical approach in gynecologic oncology in a tertiary cancer care institution.

Impact of robotic surgery on patient flow and resource use intensity in ovarian cancer

There is an emerging focus on the role of robotic surgery in ovarian cancer. To date, the operational and cost implications of the procedure remain unknown. The objective of the current study was to evaluate the impact of integrating minimally invasive robotic surgery on patient flow, resource utilization, and hospital costs associated with the treatment of ovarian cancer during the in-hospital and post-discharge processes. 261 patients operated for the primary treatment of ovarian cancer between January 2006 and November 2014 at a university-affiliated tertiary hospital were included in this study. Outcomes were compared by surgical approach (robotic vs. open surgery) as well as pre- and post-implementation of the robotics platform for use in ovarian cancer. The in-hospital patient flow and number of emergency room visits within 3 months of surgery were evaluated using multi-state Markov models and generalized linear regression models, respectively. Robotic surgery cases were associated with lower rates of postoperative complications, resulted in a more expedited postoperative patient flow (e.g., shorter time in the recovery room, ICU, and inpatient ward), and were between $10,376 and $7,421 less expensive than the average laparotomy, depending on whether or not depreciation and amortization of the robotic platform were included. After discharge, patients who underwent robotic surgery were less likely to return to the ER (IRR 0.42, p = 0.02, and IRR 0.47, p = 0.055, in the univariate and multivariable models, respectively). With appropriate use of the technology, the addition of robotics to the medical armamentarium for the management of ovarian cancer, when clinically feasible, can bring about operational efficiencies and entails cost savings.

Mandate to evaluate robotic surgery implementation: a 12-year retrospective analysis of impact and future implications

The introduction of robotic surgery in hospitals has raised much debate given the various effects on care, costs, education and medical advancement. Purchasing discussions are often approached with more questions than answers and there is a need for reports that provide a case for whether or not such technologies are advantageous from multiple perspectives, and offer insights into ways such devices can be introduced into a hospital setting. This report provides an evidence-based review of a university-affiliated tertiary care hospital's 12-year experience with robotic surgery in gynecologic oncology and delves into the various takeaways and challenges of implementing robotic surgery. Key findings were that robotic surgery significantly reduced complication rates, lengths of hospital stays for patients and overall hospital costs. Key obstacles were large upfront costs and the need for significant leadership and collaboration. Ongoing challenges to evaluating robotics include assessing long-term survival data, making comparisons with concurrently changing hospital conditions and determining how data can be generalized to other departments and institutions.