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Reducing Operating Room Turnover Time for Robotic Surgery Using a Motor Racing Pit Stop Model.
World Journal of Surgery 2017 August
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.
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.
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