Communication and Contextual Factors in Robotic-Assisted Surgical Teams: Protocol for Developing a Taxonomy
Bibliographic record
Abstract
BACKGROUND: Robotic-assisted surgery (RAS) has been rapidly integrated into surgical practice in the past few decades. The setup in the operating theater for RAS differs from that for open or laparoscopic surgery such that the operating surgeon sits at a console separate from the rest of the surgical team and the patient. Communication and team dynamics are altered due to this physical separation and visual barriers imposed by the robotic equipment. However, the factors that might comprise effective communication among members of RAS teams and the contextual factors that facilitate or inhibit effective communication in RAS remain unknown. OBJECTIVE: We aim to develop a taxonomy of communication behaviors and contextual factors that influence communication in RAS teams. We also aim to examine the patterns of communication behaviors based on gender. METHODS: We will first perform a scoping review on communication in RAS to develop a preliminary taxonomy of communication based on the existing literature. We will then conduct semistructured interviews with RAS team members, including the surgeon, assisting surgeon or trainee, bedside or first assistant, nurses, and anesthetists. Participants will represent different disciplines, including urology, general surgery, and gynecology, and have a range of experiences in RAS. We will use a reflexive thematic analysis to analyze the data and further refine the taxonomy. We will also observe live robotic surgeries at Royal College of Surgeons in Ireland (RCSI)-affiliated hospitals. We will observe varying lengths and conditions of RAS procedures to a capture a wide range of communication behaviors and contextual factors to help finalize the taxonomy. Although we anticipate conducting 30 interviews and 30 observations, we will collect data until we achieve data sufficiency. We will conduct data collection in parallel with data analysis such that if we identify a new behavior in an interview, we will follow up with questions related to that behavior in additional interviews and/or observations. RESULTS: The taxonomy from this project will include a list of actionable communication behaviors, contextual factors, their descriptions, and examples. As of May 2024, this project has been approved by the RCSI Research and Ethics Committee. Data collection started in June 2024 and will continue throughout the year. We plan to publish the findings as meaningful results emerge in our data analysis in 2024 and 2025. CONCLUSIONS: The results from this project will be used to observe and train surgical teams in a simulated environment to effectively communicate with each other and prevent communication breakdowns. The developed taxonomy will also add to the knowledge base on the role of gender in communication in RAS and produce recommendations that can be incorporated into training. Overall, this project will contribute to the improvement of communication skills of surgical teams and the quality and safety of patient care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54910.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".