Evidence map›Paper›PMID 40012663›Full record

ArticleJournal of the American College of Emergency Physicians open2025

Characterizing Emergency Department Care for Patients With Histories of Incarceration.

Thomas Huang, Vimig Socrates, Polina Ovchinnikova, Isaac Faustino, Anusha Kumar, Conrad Safranek, Ling Chi, Emily A Wang, Lisa Puglisi, Ambrose H Wong and 2 more

Abstract read
In one paragraph

Article in Journal of the American College of Emergency Physicians open, 2025. 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. Article
  3. Review
  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

12 authors.

Thomas HuangDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Vimig SocratesDepartment for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, Connecticut, USA.
Polina OvchinnikovaDepartment for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, Connecticut, USA.
Isaac FaustinoDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Anusha KumarDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Conrad SafranekDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Ling ChiDepartment for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, Connecticut, USA.
Emily A WangSEICHE Center for Health and Justice, Yale School of Medicine, New Haven, Connecticut, USA.
Lisa PuglisiSEICHE Center for Health and Justice, Yale School of Medicine, New Haven, Connecticut, USA.
Ambrose H WongDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Karen H WangDepartment for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, Connecticut, USA.
R Andrew TaylorDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Short-Term Reserch Training: Students in Health SchoolsT35HL007649 · NHLBI · YALE UNIVERSITY · PI Sarwat I Chaudhry, Erica L Herzog · 1987 to 2026
$5.7M
System Dynamics Modeling to Promote Health in Management of AgitationR01MH132605 · NIMH · YALE UNIVERSITY · PI Rebekah Heckmann, Ambrose H Wong · 2024 to 2026
$2.2M
Clinical Decision Support Tool to Assess Risk and Prevent Agitation EventsK23MH126366 · NIMH · YALE UNIVERSITY · PI WONG, AMBROSE H · 2021 to 2024
$883k
Characterizing Bias and Care Disparities with Physical Restraint Use in the Emergency Setting Using Natural Language and Cognitive DataR21MD017327 · NIMHD · YALE UNIVERSITY · PI WONG, AMBROSE H · 2022 to 2023
$461k
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS T35 HL007649NIMHD NIH HHS R21 MD017327NIMH NIH HHS K23 MH126366NIMH NIH HHS R01 MH132605
6 · The paper itself

Abstract

Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41-5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46-3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19-4.70]) and restraint use (OR: 3.76 [95% CI: 3.06-4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08-2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prevalent and subject to discrimination, in the health care system.

Indexed as

artificial intelligenceemergency medicineincarcerationlarge language modelsquality of health care

Identifiers

PMID40012663
PMCPMC11852703

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.