Abstract
Introduction:
There is a lack of protocols, formal guidance, and procedural training regarding open conversions from robot-assisted radical prostatectomy (RARP) to open radical prostatectomy (ORP). An open conversion places complex demands on the healthcare team and has recently been shown to be associated with adverse perioperative outcomes.
Aims:
To perform a root cause analysis of open conversion simulations from RARP to ORP to identify errors that may contribute to adverse events.
Methods:
From May 2013 to December 2013, with a team of two surgeons, an anesthesiologist, and three nurses, we simulated 20 emergencies during RARP that require open conversion. A human simulation model was intubated and prepared in the Trendelenburg position; a robot da Vinci SI was locked to it. All simulations were timed, transcribed, and filmed to identify errors and areas for improvement. An institutional conversion protocol was developed at the end of the conversion training.
Results:
The average conversion time was 130.9 (interquartile range [IQR] 90–201) seconds. Frequencies of the observed errors were as follows: lack of task sequence (70%), errors in robot movements (50%), loss of sterility (50%), space conflict (40%), communication errors (25%), lack of leadership (25%), and accidental fall of surgical devices (25%). Four main strategies were implemented to reduce errors: improving leadership, clearly defining roles, improving knowledge base, and surgical room reorganization. By the last simulation, conversions were performed without errors and using 55.2% less time compared with initial simulations.
Conclusions:
In this preliminary study, repeated simulations, increased leadership, improved role delineation, and surgical room reorganization enabled faster and less flawed conversions. Further studies are needed to identify if such protocols may translate to actual safety improvement during open conversions.
Introduction
T
It follows that although a rare event, a robotic team should be prepared to face a conversion to open surgery. The interaction between the da Vinci robot and a team of medical providers is potentially challenging, especially with extreme time pressures, diagnostic uncertainty, and rapidly evolving situations. However, there are no standardized intraoperative protocols for an immediate robotic surgery conversion to open surgery; team-based skills are not well defined; and potential pitfalls and errors during conversion have never been described. The aim of the present study is to perform a root cause analysis of conversion from RARP to ORP simulations to identify errors that may contribute to adverse events.
Materials and Methods
Surgical room setup
The present study took place in a 7×12 m surgical theater equipped with two surgical lights, two dual-arm ceiling supply units, one stainless cabinet, and, on one side of the room, one computer and one diaphanoscope.
The room was equipped for each simulation with the following objects: one surgical bed, a human simulation model, a robot da Vinci SI (comprising the surgeon console, image processing equipment, and surgical arm cart), two instrument tables, respectively, for the endowrist and the open surgery instruments, one Mayo stand, two bins, one suction machine, one anesthesiology chart, and four stainless chairs.
Surgical team
All the 20 simulations were performed by the same robotic team comprising a first surgeon at the robotic console, one anesthesiologist, a second surgeon, one scrub nurse, one circulating nurse, and one nurse dedicated to help either the anesthesiologist or the circulating nurse. The robotic surgery experience within the team members was, respectively, 11 years for the first surgeon, 10 years for the anesthesiologist and scrub nurse, 36 months for the second surgeon (urology resident), and more than 5 years for the other members of the team. Each healthcare provider was recognized through caps with different colors to trace their movements during the conversion attempts.
Simulations
From May 2013 to December 2013, we prospectively simulated and analyzed 20 emergencies during an RARP that required a conversion to OS. An ordinary robotic room atmosphere was recreated according to our institution setup. A human simulation model was intubated and placed in a lithotomy position with an extreme Trendelenburg. Trocars were modified and attached to the simulation model. A da Vinci robot SI was locked to the trocars. Three endowrist instruments and the camera were introduced into the trocar till a contact with the simulation model.
All the simulations were timed and filmed. In each simulation, the team had to unhook the robot, move the robot's arms and cart, reset the Trendelenburg, prepare the suction machine for open surgery, rearrange tables and instrument dispositions, and set up open surgery instruments. The clock started when the first surgeon ordered the beginning of the conversion, the clock ended when both the first and second surgeons were ready for a skin incision. Conversion protocols were developed before the first conversion attempt and then implemented during each simulation using both an internal review within the simulation team and external feedback from another urologist of the same department.
Data evaluation and statistical analysis
At the end of all the simulations, two independent physicians of the same department (F.C, A.D.G) reviewed the videos. Errors were categorized as robot movement errors (yes vs no), space conflict (yes vs no), communication errors (yes vs no), lack of leadership (yes vs no), lack of task sequence (yes vs no), loss of sterility (yes vs no), and accidental fall of surgical devices (yes vs no). Table 1 describes how we defined the errors used during the video analysis. One point was assigned for every error committed by each member of the team during the 20 simulations. Frequencies and proportion were reported for the categorical variable, while medians and interquartile ranges (IQRs) for the continuous variable. Reviewer agreement between the total errors assigned for each conversion was assessed with a Bland–Altman plot. A correlation analysis was performed to assess the relationship between the number of conversion attempts and total number of errors in relation to the time of conversion. Data were analyzed with the Statistical Package for Social Sciences software, v.20.0 (SPSS, Inc., Chicago, IL).
Results
The mean time for a conversion was 130.9 seconds (IQR 90–201). Reviewer agreement between the total errors assigned for each conversion was high. The frequencies of observed errors during the 20 conversions were 70% robot replacement errors and 50% spatial conflict and loss of the sterility, while communication errors, lack of leadership, and accidental fall of surgical devices were observed in less than 50% (Fig. 1). Furthermore, when errors were allocated to each component of the team, a higher frequency of error was attributed to the second surgeon and the first nurse (Fig. 2). The errors experienced by the second surgeon were related to the robot's movements. Conversely, errors experienced by the scrub nurse were mostly related to space conflict and lack of task sequences.
Frequencies of observed errors during the 20 conversions.
The numbers of errors for each healthcare provider.
A strong correlation between the time of conversion and number of errors was found (r=0.80, Fig. 3a). Furthermore, we found a negative correlation between the conversion attempts and the time to conversion (r = −0.89, Fig. 3b). Four main strategies during the simulations were implemented to reduce errors: improving leadership, clearly defining roles, improving knowledge base, and surgical room reorganization. In the last simulation, a conversion was performed without errors and using 55.2% less time compared with initial simulation. Table 2 describes how our practice changed after the simulation training. After a consensus was obtained from all the healthcare team members involved in the simulation tests, a department guideline for either a robotic conversion to ORP or a robotic interruption for an anesthesiological problem was developed and posted in the operating room (Fig. 4).
Correlation between
Institutional guideline for a robotic open conversion or robotic interruption.
Discussion
Conversion from RARP to ORP is a rare event, although from the nationally representative data, a conversion rate from laparoscopic radical prostatectomy to ORP was found in 1.8% (95% CI 1.4–2.1) of the total minimally invasive radical prostatectomies. The real incidence of this problem could be underestimated and only few cases in the literature describe robot and instrument malfunctioning. In a report published online in the Journal for Healthcare Quality, the Johns Hopkins team states that of the 1 million robotic surgeries performed since 2000, only 245 complications (including 71 deaths) were reported to the U.S. Food and Drug Administration (FDA). Hospitals should report these adverse events to the producer, who in turn should report them to the FDA, but this does not always happen. 7 All of these raise concerns that although rare, open conversion is something plausible and with a clinical relevance. Previous studies demonstrated how healthcare could be improved by aviation as part of progressive efforts to identify and address threats to safety in surgery. 8 –10 Aviation places considerable importance on the use of checklists, briefing and debriefing, and formal procedures to ensure the highest level of safety. On the contrary, the robotic surgery community did not focus attention to prevent complications to guarantee the best medical practice. Due to the dearth of protocols, standardized guidelines, and procedural training regarding open conversion, we aimed to analyze potential pitfalls of our surgical RARP routine.
Our preliminary study presents several interesting results. First, a reduction of 55.2% of the time of conversion was found with no errors at the last conversion. This suggests improved practitioner performance and potential benefits of the training program. The highest number of errors were committed by the second surgeon and the scrub nurse. Operator experience and extent of involvement in the conversion process may have contributed to this. Based on these data, we developed a formal guideline for internal use after an institutional consent between the anesthesiologist, surgeons, and nurses (Fig. 4). This job allocation/assignment is the first attempt to standardize an unforeseeable event during the robotic surgery and allowed a better understanding of each member's role in the team.
Finally, we determined that a correlation exists between the number of errors and the time of conversion. This is likely a consequence of an increased exposure to an unusual practice and the recognition of potential gaps in our system. Evidence in educational research is often separated into Kirkpatrick levels, with Kirkpatrick Level 1 being participant satisfaction, Level 2 representing knowledge acquisition, Level 3 being participant behavior change, and Level 4 being improved patient outcome. 11 This article would be an addition to the current Levels 2 and 3 of the simulation literature.
Although with several innovative results, our study presents some limitations. First, only complications during RARP were evaluated. Causes of open conversions are related to patient characteristics (i.e., obesity, postsurgical adhesions, anemia), surgeon variables (i.e., surgery volume and experience), and instrument/machine malfunctioning. 6,12 The increased number of RARP for patients with several comorbidities will require temporal robot detachments and the zeroing of the Trendelenburg to face immediate anesthesiology problems. For this reason, our final protocol (Fig. 4) evaluated independent emergencies that required an open conversion and others that required a detachment of the robot and a hemodynamic stabilization of the patient. Second, no control group was considered and further comparison evaluations are needed. Third, the overall time of simulated conversion was shorter than expected. An emergency during an actual operation typically takes 10 minutes from the decision to resume open surgery. In addition, simulations are widely different from reality, especially in a crisis scenario. Finally, difference in experience among members of the healthcare team was present in the study.
It would be interesting to apply this protocol to other robotic urological operations to see if similar trends would be observed.
Conclusions
Repeated simulations, increased leadership, improved role delineation, and surgical room reorganization enable faster and more appropriate conversions. Further investigations are needed to identify if the above-mentioned strategies may improve patient intraoperative and postoperative outcomes.
Footnotes
Acknowledgments
F.Z. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design were done by F.D.M. and F.Z.; acquisition of data by A.D.G. and F.C.; drafting of the manuscript by F.Z. and A.G.; analysis and interpretation of data by F.Z. and A.G.; statistical analysis by F.Z. and M.M.; critical revision of the manuscript for important intellectual content by A.G., A.C., F.V., and C.V.; administrative, technical, or material support by F.Z. and F.D.M.; supervision by F.Z. and F.D.M.; and language editing by (Heather Werenski, Nikhil Prasad).
Author Disclosure Statement
No competing financial interests exist.