Surgical Endoscopy

, Volume 30, Issue 9, pp 3749–3761 | Cite as

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

  • Ken CatchpoleEmail author
  • Colby Perkins
  • Catherine Bresee
  • M. Jonathon Solnik
  • Benjamin Sherman
  • John Fritch
  • Bruno Gross
  • Samantha Jagannathan
  • Niv Hakami-Majd
  • Raymund Avenido
  • Jennifer T. Anger



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.


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).


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.


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.


Robotic surgery Human Factors Error Safety Teamwork Automation 



This was funded by National Institute of Biomedical Imaging & Biomedical Engineering Award R03EB017447 (Catchpole/Anger) and the UCLA Medical Student Training in Aging Research Program- the National Institute on Aging (T35AG026736), the John A. Hartford Foundation, and the Lillian R. Gleitsman Foundation. Our sincere thanks to all the surgeons, OR staff and residents who participated and allowed us to observe their operations.

Compliance with ethical standards


Dr. Catchpole has received funding from Medtronic Ltd, and received funding to attend a meeting unrelated to this project at Intuitive Surgical headquarters. Colby Perkins, Catherine Bresee, M. Jonathon Solnik, Benjamin Sherman, John Fritchhas, Bruno Grosshas, Samantha Jagannathan, Niv Hakami-Majdhas, Raymund Avenido and Jennifer T. Anger: None.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Ken Catchpole
    • 1
    Email author
  • Colby Perkins
    • 1
    • 2
  • Catherine Bresee
    • 3
  • M. Jonathon Solnik
    • 4
  • Benjamin Sherman
    • 5
  • John Fritch
    • 5
  • Bruno Gross
    • 5
  • Samantha Jagannathan
    • 5
  • Niv Hakami-Majd
    • 5
  • Raymund Avenido
    • 1
  • Jennifer T. Anger
    • 1
  1. 1.Department of SurgeryCedars-Sinai Medical CenterLos AngelesUSA
  2. 2.David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA
  3. 3.Biostatistics and Bioinformatics Research InstituteCedars-Sinai Medical CenterLos AngelesUSA
  4. 4.Department of Obstetrics and GynecologyCedars-Sinai Medical CenterLos AngelesUSA
  5. 5.Medical Student Training in Aging Research (MSTAR) ProgramUniversity of CaliforniaLos AngelesUSA

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