Evidence map›Paper›PMID 33560239›Full record

ArticleJMIR research protocols2021

Using Flow Disruptions to Examine System Safety in Robotic-Assisted Surgery: Protocol for a Stepped Wedge Crossover Design.

Myrtede C Alfred, Tara N Cohen, Kate A Cohen, Falisha F Kanji, Eunice Choi, John Del Gaizo, Lynne S Nemeth, Alexander V Alekseyenko, Daniel Shouhed, Stephen J Savage and 2 more

Abstract read
In one paragraph

Article in JMIR research protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Observational
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

12 authors.

Myrtede C AlfredMedical University of South Carolina, Department of Anesthesia and Perioperative Medicine, Charleston, SC, United States.ORCID https://orcid.org/0000-0003-0045-0426
Tara N CohenCedars-Sinai Medical Center, Department of Surgery, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-2137-6093
Kate A CohenCedars-Sinai Medical Center, Department of Surgery, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-1522-0028
Falisha F KanjiCedars-Sinai Medical Center, Department of Surgery, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-0163-2782
Eunice ChoiCedars-Sinai Medical Center, Department of Surgery, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-6084-8917
John Del GaizoMedical University of South Carolina, Biomedical Informatics Center, Charleston, SC, United States.ORCID https://orcid.org/0000-0003-1910-2579
Lynne S NemethMedical University of South Carolina, College of Nursing, Charleston, SC, United States.ORCID https://orcid.org/0000-0001-8691-1400
Alexander V AlekseyenkoMedical University of South Carolina, Biomedical Informatics Center, Charleston, SC, United States.ORCID https://orcid.org/0000-0002-5748-2085
Daniel ShouhedCedars-Sinai Medical Center, Department of Surgery, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-6577-0266
Stephen J SavageDepartment of Urology, Medical University of South Carolina, Carleston, SC, United States.ORCID https://orcid.org/0000-0002-5132-4377
Jennifer T AngerCedars-Sinai Medical Center, Department of Surgery, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-3897-5941
Ken CatchpoleMedical University of South Carolina, Department of Anesthesia and Perioperative Medicine, Charleston, SC, United States.ORCID https://orcid.org/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 integration of high technology into health care systems is intended to provide new treatment options and improve the quality, safety, and efficiency of care. Robotic-assisted surgery is an example of high technology integration in health care, which has become ubiquitous in many surgical disciplines.

objectiveThis study aims to understand and measure current robotic-assisted surgery processes in a systematic, quantitative, and replicable manner to identify latent systemic threats and opportunities for improvement based on our observations and to implement and evaluate interventions. This 5-year study will follow a human factors engineering approach to improve the safety and efficiency of robotic-assisted surgery across 4 US hospitals.

methodsThe study uses a stepped wedge crossover design with 3 interventions, introduced in different sequences at each of the hospitals over four 8-month phases. Robotic-assisted surgery procedures will be observed in the following specialties: urogynecology, gynecology, urology, bariatrics, general, and colorectal. We will use the data collected from observations, surveys, and interviews to inform interventions focused on teamwork, task design, and workplace design. We intend to evaluate attitudes toward each intervention, safety culture, subjective workload for each case, effectiveness of each intervention (including through direct observation of a sample of surgeries in each observational phase), operating room duration, length of stay, and patient safety incident reports. Analytic methods will include statistical data analysis, point process analysis, and thematic content analysis.

resultsThe study was funded in September 2018 and approved by the institutional review board of each institution in May and June of 2019 (CSMC and MDRH: Pro00056245; VCMC: STUDY 270; MUSC: Pro00088741). After refining the 3 interventions in phase 1, data collection for phase 2 (baseline data) began in November 2019 and was scheduled to continue through June 2020. However, data collection was suspended in March 2020 due to the COVID-19 pandemic. We collected a total of 65 observations across the 4 sites before the pandemic. Data collection for phase 2 was resumed in October 2020 at 2 of the 4 sites.

conclusionsThis will be the largest direct observational study of surgery ever conducted with data collected on 680 robotic surgery procedures at 4 different institutions. The proposed interventions will be evaluated using individual-level (workload and attitude), process-level (perioperative duration and flow disruption), and organizational-level (safety culture and complications) measures. An implementation science framework is also used to investigate the causes of success or failure of each intervention at each site and understand the potential spread of the interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25284.

Indexed as

crossover designergonomicspatient safetyrobotic surgical procedures

Identifiers

PMID33560239
PMCPMC7902184

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.