Evidence map›Paper›PMID 34898324›Full record

ArticleJournal of intensive care medicine2022

Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma).

Vincent I Lau, Alexandra Binnie, John Basmaji, Nadia Baig, Dawn Opgenorth, Saoirse Cameron, Katie O'Hearn, Ellen McDonald, Janek Senaratne, Wendy Sligl and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of intensive care medicine, 2022. 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. Trial
  2. Article
  3. Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning.Journal of vascular surgery cases and innovative techniques · 2022
    Article
  4. 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

14 authors.

Vincent I Lau12357University of Alberta and Alberta Health Services, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0002-9939-7348
Alexandra Binnie60407William Osler Health System, Brampton, ON, Canada.
John Basmaji6221Western University, London, ON, Canada.
Nadia Baig12357University of Alberta and Alberta Health Services, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0002-2112-5393
Dawn Opgenorth12357University of Alberta and Alberta Health Services, Edmonton, Alberta, Canada.
Saoirse Cameron10033London Health Sciences Center, London, ON, Canada.
Katie O'Hearn274065Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada.
Ellen McDonald3710McMaster University, Hamilton, ON, Canada.
Janek Senaratne12357University of Alberta and Alberta Health Services, Edmonton, Alberta, Canada.
Wendy Sligl12357University of Alberta and Alberta Health Services, Edmonton, Alberta, Canada.
Danny J Zuege70401University of Calgary, Calgary, Alberta, Canada.
Oleksa Rewa12357University of Alberta and Alberta Health Services, Edmonton, Alberta, Canada.
Sean M Bagshaw12357University of Alberta and Alberta Health Services, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0003-3633-6596
Jennifer Tsang3710McMaster University, Hamilton, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCritical care research in Canada is conducted primarily in academically-affiliated intensive care units with established research infrastructure, including research coordinators (RCs). Recently, efforts have been made to engage community hospital ICUs in research albeit with barriers. Automation or artificial intelligence (AI) could aid the performance of routine research tasks. It is unclear which research study processes might be improved through AI automation.

methodsWe conducted a cross-sectional survey of Canadian ICU research personnel. The survey contained items characterizing opinions regarding research processes that may be amenable to AI automation. We distributed the questionnaire via email distribution lists of 3 Canadian research societies. Open-ended questions were analyzed using a thematic content analysis approach.

resultsA total of 49 survey responses were received (response rate: 8%). Tasks that respondents felt were time-consuming/tedious/tiresome included: screening for potentially eligible patients (74%), inputting data into case report forms (65%), and preparing internal tracking logs (53%). Tasks that respondents felt could be performed by AI automation included: screening for eligible patients (59%), inputting data into case report forms (55%), preparing internal tracking logs (51%), and randomizing patients into studies (45%). Open-ended questions identified enthusiasm for AI automation to improve information accuracy and efficiency while freeing up RCs to perform tasks that require human interaction. This enthusiasm was tempered by the need for proper AI education and oversight.

conclusionsThere were balanced supportive (increased efficiency and re-allocation of tasks) and challenges (informational accuracy and oversight) with regards to AI automation in ICU research.

Indexed as

Artificial IntelligenceIntensive Care UnitsAutomationCanadaCross-Sectional StudiesHumansNeeds Assessmentartificial intelligenceautomationcritical careefficiencyneeds assessmentresearchsurvey

Identifiers

PMID34898324
PMCPMC9468938

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.