The Utility of Automated, Data-Driven Clinical Support in Telemedicine for Critical Illness: A Survey-Based Study.

Andre L Holder, Fiona Winterbottom, Desiree E Kosmisky, Jayashree K Raikhelkar, Ahmed S Ahmed, Mohamed A Mahmoud, Krzysztof Laudanski

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In one paragraph

Article in Critical care explorations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Andre L HolderDivision of Pulmonary, Allergy, Critical Care & Sleep Medicine, Emory University School of Medicine, Atlanta, GA.

Fiona WinterbottomDepartment of Pulmonology, Ochsner Health, New Orleans, LA.

Desiree E KosmiskyDepartment of Pharmacy, Virtual Critical Care, Atrium Health, Mint Hill, NC.

Jayashree K RaikhelkarDepartment of Anesthesiology and Critical Care, Emory University School of Medicine, Atlanta, GA.

Ahmed S AhmedDepartment of Anesthesiology and Perioperative Care, Mayo Clinic, Rochester, MN.

Mohamed A MahmoudDepartment of Anesthesiology and Perioperative Care, Mayo Clinic, Rochester, MN.

Krzysztof LaudanskiDepartment of Anesthesiology and Perioperative Care, Mayo Clinic, Rochester, MN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A growing number of data-driven clinical decision support (CDS) tools are incorporated into tele-critical care, but the clinician perceptions of their utility are largely unknown. The objective of this web-based survey study was to understand the perceived utility of data-driven CDS in tele-critical care. The survey had 158 respondents (1.1% response rate), with 51.3% stating they currently use a data-driven CDS tool. Of those who responded about the impact of data-driven CDS, most (62.0%) reported a meaningful impact on workup, evidence-based care, or patient outcomes. Survey participants found CDS most useful if they or their colleagues had positive experiences with it, especially if it was responsible for improved patient outcomes. Thus, data-driven CDS is perceived useful for tele-critical care services.

Indexed as

Critical CareCritical IllnessDecision Support Systems, ClinicalTelemedicineHumansSurveys and Questionnairesartificial intelligenceclinical decision supportemerging technologiesimplementationmachine learning algorithmstele-critical care

Identifiers

PMID41738600

DOI10.1097/CCE.0000000000001374

PMCPMC12944116

What OpenQuestion holds

Textmetadata

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.