Evidence map›Paper›PMID 33469695›Full record

ArticleSurgical endoscopy2022

Barriers to safety and efficiency in robotic surgery docking.

Lucy Cofran, Tara Cohen, Myrtede Alfred, Falisha Kanji, Eunice Choi, Stephen Savage, Jennifer Anger, Ken Catchpole

Abstract read
In one paragraph

Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. [Anesthesia for robot-assisted surgery].Die Anaesthesiologie · 2026
    Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Observational
  8. Review
  9. Robot-Assisted Minimally Invasive Esophagectomy (RAMIE) vs. Conventional Minimally Invasive Esophagectomy (MIE) for Esophageal Cancer: A Nationwide Inpatient Sample Analysis from 2017 to 2020.Annals of thoracic and cardiovascular surgery : official journal of the Association of Thoracic and Cardiovascular Surgeons of Asia · 2025
    Article
  10. Article
  11. Review
  12. Workflow disruptions in robot-assisted surgery.Journal of robotic surgery · 2023
    Review
  13. Automatic patient positioning based on robot rotational workspace for extended reality.International journal of computer assisted radiology and surgery · 2023
    Article
  14. Article
  15. Article
  16. Observational
  17. Article
  18. Article
  19. Room Size Influences Flow in Robotic-Assisted Surgery.International journal of environmental research and public health · 2021
    Article
  20. 'Rise of the Machines': Human Factors and training for robotic-assisted surgery.BMJ surgery, interventions, & health technologies · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Lucy CofranDepartment of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, SC, USA.
Tara CohenDepartment of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Myrtede AlfredDepartment of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, SC, USA.
Falisha KanjiDepartment of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Eunice ChoiDepartment of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Stephen SavageDepartment of Urology, Medical University of South Carolina, Charleston, SC, USA.
Jennifer AngerCedars-Sinai Medical Center, Los Angeles, CA, USA.
Ken CatchpoleDepartment of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, SC, USA. catchpol@musc.edu.ORCID 0000-0003-4073-3025

Funding

Human Factors and Systems Integration in High Technology Surgery (HF-SIgHTS)R01HS026491 · AHRQ · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI ANGER, JENNIFER TASH, CATCHPOLE, KEN · 2018 to 2022
$2.0M
AHRQ HHS R01 HS026491
6 · The paper itself

Abstract

backgroundThe introduction of new technology into the operating room (OR) can be beneficial for patients, but can also create new problems and complexities for physicians and staff. The observation of flow disruptions (FDs)-small deviations from the optimal course of care-can be used to understand how systems problems manifest. Prior studies showed that the docking process in robotic assisted surgery (RAS), which requires careful management of process, people, technology and working environment, might be a particularly challenging part of the operation. We sought to explore variation across multiple clinical sites and procedures; and to examine the sources of those disruptions.

methodsTrained observers recorded FDs during 45 procedures across multiple specialties at three different hospitals. The rate of FDs was compared across surgical phases, sites, and types of procedure. A work-system flow of the RAS docking procedure was used to determine which steps were most disrupted.

resultsThe docking process was significantly more disrupted than other procedural phases, with no effect of hospital site, and a potential interaction with procedure type. Particular challenges were encountered in room organization, retrieval of supplies, positioning the patient, and maneuvering the robot.

conclusionsDirect observation of surgical procedures can help to identify approaches to improve the design of technology and procedures, the training of staff, and configuration of the OR environment, with the eventual goal of improving safety, efficiency and teamwork in high technology surgery.

Indexed as

Robotic Surgical ProceduresEfficiencyHumansOperating RoomsWorkflowFlow disruptionsHuman factorsRoboticsSafetySurgeryTeamwork

Identifiers

PMID33469695
PMCPMC8286975

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.