Evidence map›Paper›PMID 39310343›Full record

ArticleAnnals of surgery open : perspectives of surgical history, education, and clinical approaches2024

Consensus for Operating Room Multimodal Data Management: Identifying Research Priorities for Data-Driven Surgery.

Alain Garcia Vazquez, Juan Verde, Ariosto Hernandez Lara, Didier Mutter, Lee Swanstrom

Abstract read
In one paragraph

Article in Annals of surgery open : perspectives of surgical history, education, and clinical approaches, 2024. 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. Review
  2. Review
  3. Review
  4. Review
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

5 authors.

Alain Garcia VazquezFrom the Institute of Image Guided Surgery of Strasbourg, IHU Strasbourg, Strasbourg, France.
Juan VerdeFrom the Institute of Image Guided Surgery of Strasbourg, IHU Strasbourg, Strasbourg, France.
Ariosto Hernandez LaraFrom the Institute of Image Guided Surgery of Strasbourg, IHU Strasbourg, Strasbourg, France.
Didier MutterInstitute of Image Guided Surgery of Strasbourg, IHU Strasbourg, Hôpitaux Universitaires de Strasbourg and IRCAD Strasbourg, Strasbourg, France.
Lee SwanstromFrom the Institute of Image Guided Surgery of Strasbourg, IHU Strasbourg, Strasbourg, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aimed to identify research areas that demand attention in multimodal data-driven surgery for improving data management in minimally invasive surgery. Background: New surgical procedures, high-tech equipment, and digital tools are increasingly being introduced, potentially benefiting patients and surgical teams. These innovations have resulted in operating rooms evolving into data-rich environments, which, in turn, requires a thorough understanding of the data pipeline for improved and more intelligent real-time data usage. As this new domain is vast, it is necessary to identify where efforts should be focused on developing seamless and practical data usage. Methods: A modified electronic Delphi approach was used; 53 investigators were divided into the following groups: a research group (n=9) for problem identification and a narrative literature review, a medical and technical expert group (n=14) for validation, and an invited panel (n=30) for two electronic survey rounds. Round 1 focused on a consensus regarding bottlenecks in surgical data science areas and research gaps, while round 2 prioritized the statements from round 1, and a roadmap was created based on the identified essential and very important research gaps. Results: Consensus panelists have identified key research areas, including digitizing operating room (OR) activities, improving data streaming through advanced technologies, uniform protocols for handling multimodal data, and integrating AI for efficiency and safety. The roadmap prioritizes standardizing OR data formats, integrating OR data with patient information, ensuring regulatory compliance, standardizing surgical AI models, and securing data transfers in the next generation of wireless networks. Conclusions: This work is an international expert consensus regarding the current issues and key research targets in the promising field of data-driven surgery, highlighting the research needs of many operating room stakeholders with the aim of facilitating the implementation of novel patient care strategies in minimally invasive surgery.

Indexed as

data-driven surgerydigital surgerymachine learning in surgerysurgical data managementsurgical data science

Identifiers

PMID39310343
PMCPMC11415082

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.