Robots allow a broad range of surgeons to utilize a laparoscopic approach successfully for many complex operations. As a consequence, the use of robotic procedures performed worldwide has nearly tripled since 2007. However, there is a substantial learning curve associated with the use of surgical robots that may depend on elements other than surgical technique. In fact, high quality, efficient and safe care arises through complex interactions between operating room staff, tasks, technology, environment and organization. Despite an increasing evidence base in other surgeries, there is an absence of published studies examining surgical robotics within the context of these systems models. Our goal is to apply human factors systems analysis techniques to robotic surgery in order to better understand where care is inefficient or suboptimal. We will capture flow disruptions - small errors or deviations from care - which will lay the foundation for an intervention that seeks to improve the flow of robotic surgery, and thus enhance the safety, quality, efficiency and learning associated with surgical robotics.
In Specific Aim 1, we seek to identify and define flow disruptions that occur during robotic surgery. We will do this through consecutive observations of 100 robotic operations at Cedars- Sinai Medical Center.
In Specific Aim 2, we seek to identify technical and non-technical factors that contribute to flow disruptions which may impact the surgeon learning curve. We seek to determine how flow varies by procedure type and by surgeon volume. We will also determine whether patient factors impact rates of flow disruptions. We will use multi-variable statistical analysis to analyze the rate of flow disruptions by surgeon volume and specialty, patient factors (such as comorbidities and body mass index), operation type, and other factors we may identify throughout the observation process.
In Specific Aim 3, we seek to perform a pilot intervention that will assist in reducing flow disruptions. We will firs conduct a qualitative analysis in which we group disruptions together that relate to similar causes or system problems. Finally, through discussions with human factors experts and surgeons, we will develop a range of solutions that will be put in place and tested as a pilot intervention. We will then measure the preliminary effect of this intervention through the observation and analysis of five robotic cases.
The use of surgical robots has increased dramatically in recent years, as has our understanding of the relationship between surgical expertise, teamwork, and technology in more traditional surgeries. The present research seeks to understand these relationships in robotic surgery, and thus to identify opportunities to improve performance, reduce errors, and shorten the surgical learning curve.
| Catchpole, Ken; Bisantz, Ann; Hallbeck, M Susan et al. (2018) Human factors in robotic assisted surgery: Lessons from studies 'in the Wild'. Appl Ergon : |
| Catchpole, Ken R; Hallett, Elyse; Curtis, Sam et al. (2018) Diagnosing barriers to safety and efficiency in robotic surgery. Ergonomics 61:26-39 |
| Souders, Colby P; Catchpole, Ken R; Wood, Lauren N et al. (2017) Reducing Operating Room Turnover Time for Robotic Surgery Using a Motor Racing Pit Stop Model. World J Surg 41:1943-1949 |
| Jain, Monica; Fry, Brian T; Hess, Luke W et al. (2016) Barriers to efficiency in robotic surgery: the resident effect. J Surg Res 205:296-304 |
| Catchpole, Ken; Perkins, Colby; Bresee, Catherine et al. (2016) Safety, efficiency and learning curves in robotic surgery: a human factors analysis. Surg Endosc 30:3749-61 |
