Artificial Intelligence and Machine Learning for Emergency Department Overcrowding: A Systematic Review with Large Language Model-Assisted Screening.

Zekai Wang, Ahmed Qasem, Lin Lu, Bunyamin Ozaydin, Abdulaziz Ahmed

Abstract readReview

In one paragraph

Review in Healthcare (Basel, Switzerland), 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

5 authors.

Zekai WangDepartment of Analytics, Charles F. Dolan School of Business, Fairfield University, 1073 North Benson Road, Fairfield, CT 06824, USA.ORCID 0009-0005-8028-2582

Ahmed QasemDepartment of Health Services Administration, School of Health Professions, The University of Alabama, 1720 2nd Avenue South, Birmingham, AL 35294, USA.ORCID 0000-0002-2386-2944

Lin LuDepartment of Analytics, Charles F. Dolan School of Business, Fairfield University, 1073 North Benson Road, Fairfield, CT 06824, USA.ORCID 0000-0001-6571-5064

Bunyamin OzaydinDepartment of Health Services Administration, School of Health Professions, The University of Alabama, 1720 2nd Avenue South, Birmingham, AL 35294, USA.ORCID 0000-0002-6775-0450

Abdulaziz AhmedDepartment of Health Services Administration, School of Health Professions, The University of Alabama, 1720 2nd Avenue South, Birmingham, AL 35294, USA.ORCID 0000-0001-6081-8507

Funding

AHRQ HHS 1R21HS029410-01A1AHRQ HHS R21 HS029410

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

artificial intelligenceemergency department overcrowdinglarge language modelslength of staymachine learningpatient flowpredictive modelingsystematic review

Identifiers

PMID42736925

DOI10.3390/healthcare14172767

PMCPMC13565782

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.