The impact of resident training on robotic operative times: is there a July Effect?

It is unknown whether the July Effect (a theory that medical errors and organizational inefficiencies increase during the influx of new surgical residents) exists in urologic robotic-assisted surgery. The aim of this study was to investigate the impact of urology resident training on robotic operative times at the beginning of the academic year. A retrospective chart review was conducted for urologic robotic surgeries performed at a single institution between 2008 and 2019. Univariate and multivariate mix model analyses were performed to determine the association between operative time and patient age, estimated blood loss, case complexity, robotic surgical system (Si or Xi), and time of the academic year. Differences in surgery time and non-surgery time were assessed with/without resident presence. Operative time intervals were included in the analysis. Resident presence correlated with increased surgery time (38.6 min (p < 0.001)) and decreased non-surgery time (4.6 min (p < 0.001)). Surgery time involving residents decreased by 8.7 min after 4 months into the academic year (July-October), and by an additional 5.1 min after the next 4 months (p = 0.027, < 0.001). When compared across case types stratified by complexity, surgery time for cases with residents significantly varied. Cases without residents did not demonstrate such variability. Resident presence was associated with prolonged surgery time, with the largest effect occurring in the first 4 months and shortening later in the year. However, resident presence was associated with significantly reduced non-surgery time. These results help to understand how new trainees impact operating room times.

The Application of Human Factors Engineering to Reduce Operating Room Turnover in Robotic Surgery.

BACKGROUND: Challenges associated with turnover time are magnified in robotic surgery. The introduction of advanced technology increases the complexity of an already intricate perioperative environment. We applied a human factors approach to develop systematic, data-driven interventions to reduce robotic surgery turnover time. METHODS: Researchers observed 40 robotic surgery turnovers at a tertiary hospital [20 pre-intervention (Jan 2018 to Apr 2018), 20 post-intervention (Jan 2019 to Jun 2019)]. Components of turnover time, including cleaning, instrument and room set-up, robot preparation, flow disruptions, and major delays, were documented and analyzed. Surveys and focus groups were used to investigate staff perceptions of robotic surgery turnover time. A multidisciplinary team of human factors experts and physicians developed targeted interventions. Pre- and post-intervention turnovers were compared. RESULTS: Median turnover time was 67 min (mean: 72, SD: 24) and 22 major delays were noted (1.1/case). The largest contributors were instrument setup (25.5 min) and cleaning (25 min). Interventions included an electronic dashboard for turnover time reporting, clear designation of roles and simultaneous completion of tasks, process standardization of operating room cleaning, and data transparency through monthly reporting. Post-intervention turnovers were significantly shorter (U = 57.5, p = .000) and ten major delays were noted. CONCLUSIONS: Human factors analysis generated interventions to improve turnover time. Significant improvements were seen post-intervention with a reduction in turnover time by a 26 min and decrease in major delays by over 50%. Future opportunities to intervene and further improve turnover time include targeting pre- and post-operative care phases.

Discovering the barriers to efficient robotic operating room turnover time: perceptions vs. reality.

Turnover time (TOT) has remained the subject of numerous research articles and operating room (OR) committee discussions. Inefficiencies associated with TOT are multiplied when complex technology, such as surgical robots, is involved. Using a human factors approach, this study investigated impediments to efficient robotic TOT and team members' perceptions surrounding this topic. Researchers observed 20 robotic turnovers over 2 months at a tertiary hospital. TOT, cleaning time, number of staff present, bed set-up time, instrument set-up time and any major delays were recorded. Additionally, 79 OR team members completed a questionnaire regarding perceptions of OR turnover. Average TOT was 72 min (s, 24 min). Overall, cleaning required the most time (average of 27.4 min, 37.96% of TOT), followed by instrument set-up (15.4 min, 21.34% of TOT) and RN retrieval of the patient from pre-op (12 min, 17.72% of TOT). OR team members estimated that turnovers require 60.36 min. Physicians believed the greatest contributor to TOT was "time to set up the OR", while OR staff rated "instrument availability" as the greatest issue, both of which were inaccurate. OR team members' perceptions of robotic TOT and contributing factors were different from reality based on observed contributors. Data demonstrated several areas of opportunity for process improvement. These data can be used to guide the implementation of targeted interventions to improve TOT efficiency.

Reducing Operating Room Turnover Time for Robotic Surgery Using a Motor Racing Pit Stop Model.

BACKGROUND: Operating room (OR) turnover time, time taken between one patient leaving the OR and the next entering, is an important determinant of OR utilization, a key value metric for hospital administrators. Surgical robots have increased the complexity and number of tasks required during an OR turnover, resulting in highly variable OR turnover times. We sought to streamline the turnover process and decrease robotic OR turnover times and increase efficiency. METHODS: Direct observation of 45 pre-intervention robotic OR turnovers was performed. Following a previously successful model for handoffs, we employed concepts from motor racing pit stops, including briefings, leadership, role definition, task allocation and task sequencing. Turnover task cards for staff were developed, and card assignments were distributed for each turnover. Forty-one cases were observed post-intervention. RESULTS: Average total OR turnover time was 99.2 min (95% CI 88.0-110.3) pre-intervention and 53.2 min (95% CI 48.0-58.5) at 3 months post-intervention. Average room ready time from when the patient exited the OR until the surgical technician was ready to receive the next patient was 42.2 min (95% CI 36.7-47.7) before the intervention, which reduced to 27.2 min at 3 months (95% CI 24.7-29.7) post-intervention (p < 0.0001). CONCLUSIONS: Role definition, task allocation and sequencing, combined with a visual cue for ease-of-use, create efficient, and sustainable approaches to decreasing robotic OR turnover times. Broader system changes are needed to capitalize on that result. Pit stop and other high-risk industry models may inform approaches to the management of tasks and teams.

Safety, efficiency and learning curves in robotic surgery: a human factors analysis.

BACKGROUND: Expense, efficiency of use, learning curves, workflow integration and an increased prevalence of serious incidents can all be barriers to adoption. We explored an observational approach and initial diagnostics to enhance total system performance in robotic surgery. METHODS: Eighty-nine robotic surgical cases were observed in multiple operating rooms using two different surgical robots (the S and Si), across several specialties (Urology, Gynecology, and Cardiac Surgery). The main measures were operative duration and rate of flow disruptions-described as 'deviations from the natural progression of an operation thereby potentially compromising safety or efficiency.' Contextual parameters collected were surgeon experience level and training, type of surgery, the model of robot and patient factors. Observations were conducted across four operative phases (operating room pre-incision; robot docking; main surgical intervention; post-console). RESULTS: A mean of 9.62 flow disruptions per hour (95 % CI 8.78-10.46) were predominantly caused by coordination, communication, equipment and training problems. Operative duration and flow disruption rate varied with surgeon experience (p = 0.039; p < 0.001, respectively), training cases (p = 0.012; p = 0.007) and surgical type (both p < 0.001). Flow disruption rates in some phases were also sensitive to the robot model and patient characteristics. CONCLUSIONS: Flow disruption rate is sensitive to system context and generates improvement diagnostics. Complex surgical robotic equipment increases opportunities for technological failures, increases communication requirements for the whole team, and can reduce the ability to maintain vision in the operative field. These data suggest specific opportunities to reduce the training costs and the learning curve.