Evidence map›Paper›PMID 40434774›Full record

ArticleJAMA network open2025

Emergency Department Boarding, Inpatient Census, and Interhospital Transfer Acceptances.

Margaret Greenwood-Ericksen, Neil Kamdar, Kjirsten Swenson, Peter Pruitt, Marta L McCrum, Gabriel Paul, Larissa Myaskovsky, Keith E Kocher, Kori S Zachrison

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Margaret Greenwood-EricksenDepartment of Emergency Medicine, School of Medicine, University of New Mexico, Albuquerque.
Neil KamdarInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor.
Kjirsten SwensonDepartment of Emergency Medicine, School of Medicine, University of New Mexico, Albuquerque.
Peter PruittDepartment of Emergency Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Marta L McCrumDepartment of Surgery, University of Utah, Salt Lake City.
Gabriel PaulDepartment of Emergency Medicine, School of Medicine, University of New Mexico, Albuquerque.
Larissa MyaskovskyDepartment of Internal Medicine, University of New Mexico, School of Medicine, Albuquerque.
Keith E KocherDepartments of Emergency Medicine and Learning Health Sciences, University of Michigan, Ann Arbor.
Kori S ZachrisonDepartment of Emergency Medicine, Massachusetts General Hospital, Boston.

Funding

Addressing Racial/Ethnic Disparities in Stroke Care Access Using Hospital Transfer NetworksR01AG079887 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Korilyn Sauser Zachrison · 2023 to 2026
$2.9M
AHRQ HHS K08 HS029038NIA NIH HHS R01 AG079887
6 · The paper itself

Abstract

Importance: Referral hospitals in the US are experiencing unprecedented levels of crowding, leading them to increasingly refuse interhospital transfer (IHT) requests. Crowded hospitals are dangerous, but refusing IHTs undermines the role of referral hospitals and may cause harm. Objective: To measure associations of hospital crowding measures (emergency department [ED] boarding and inpatient census) with IHT acceptances overall and for prioritized conditions. Design, Setting, and Participants: This cross-sectional study from January 2019 to May 2023 analyzed data from the only academic and level I trauma center in a highly rural state in the Southwestern US, including transfer center data, ED boarding hours, and inpatient census. All transfer center calls regarding adults (age >18 years) were eligible for the study. Data were analyzed from June to October 2024. Main Outcome and Measures: The primary outcome was the proportion of transfer requests accepted on a weekly and monthly basis. Adjusted logistic regression was used to analyze associations of ED boarding time and inpatient census with IHT acceptance, considering prioritized conditions (obstetrics, ST-elevation myocardial infarction [STEMI], stroke, and trauma) and rurality. Transfer data contained IHT request descriptors, including referring facility, date and time of call, decision (accept or decline), diagnosis, and patient demographics. ED boarding was measured daily as a sum of all boarding hours for each ED patient. Results: The study included 26 020 IHT requests (11 267 women [43.2%]; mean [SD] age, 54.4 [19.6] years), of which 16 062 were accepted (61.7%). There were 22 119 (85.0%) requests from urban and 3901 requests (15.0%) from rural hospitals, with the majority of IHT requests (19 912 requests [76.3%]) seeking transfer from an ED. There was a negative correlation between IHT acceptance and ED boarding (Pearson r, -0.73) and inpatient census (Pearson r, -0.87). At times of worst ED boarding (highest vs lowest quartile), the odds of IHT acceptance were lower (adjusted odds ratio [aOR], 0.71; 95% CI, 0.66-0.78). Of the 3901 rural requests, 2196 (56.3%) were accepted, with lower odds of acceptance for rural vs urban requests (aOR, 0.66; 95% CI, 0.64-0.79). Prioritized diagnoses were more commonly accepted, particularly obstetrics (aOR, 5.28; 95% CI, 4.17-6.70), STEMI (aOR, 3.04; 95% CI, 1.86-4.98), and trauma (aOR, 3.19; 95% CI, 2.86, 3.57). Conclusions and Relevance: In this cross-sectional study of IHT requests, the severity of ED boarding and inpatient census were associated with decreased IHT acceptance, suggesting that overcrowded referral hospitals face tradeoffs as they seek to fulfill seemingly conflicting obligations to safely care for locally hospitalized patients and accept regional patients seeking transfer.

Indexed as

CrowdingEmergency Service, HospitalInpatientsPatient TransferAdultAgedCensusesCross-Sectional StudiesFemaleHumansMaleMiddle Aged

Identifiers

PMID40434774
PMCPMC12120653

What OpenQuestion holds

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LicenceCC BY
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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.