MétaCan
Menu
Back to cohort
Record W4413469990 · doi:10.2196/75865

Influences on Emergency Clinician Use of Health Information Exchange: Interview Study

2025· article· en· W4413469990 on OpenAlexvenueno aff
Brian E. Dixon, Umesh Ghimire, Benjamin Richter

Bibliographic record

VenueJMIR Medical Informatics · 2025
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
FundersAgency for Healthcare Research and Quality
KeywordsPreprintHealth information exchangeMedicineMedical emergencyQualitative researchHealth careInformation exchangeData scienceComputer scienceHealth informationWorld Wide WebTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: Health information exchange (HIE) supports clinical decision-making in emergency medicine settings. Despite evidence and policies that encourage the adoption of HIE, use by clinicians is limited. Moreover, few studies examine HIE use years after adoption by hospitals or clinics. OBJECTIVE: This study aims to examine the perceptions and use of a mature, operational HIE system by emergency department clinicians years after its implementation. METHODS: We interviewed 21 clinicians in various roles (eg, attending physician and nurse practitioner) across multiple health systems that participate in a statewide HIE network. We asked questions about their use of the HIE system and the factors that facilitate or inhibit use. Analysis of interview transcripts was guided by a theoretical framework derived from information systems theories describing individual perception of, and use behavior toward, HIE systems. RESULTS: A total of 26 factors across 6 domains were identified by respondents. All respondents recognized the value of HIE for medical decision-making in the emergency department, and access to information via the HIE system was preferred over traditional methods of telephoning other facilities or waiting for faxed records. Ease of use, particularly single sign-on functionality, was recognized as a key facilitator of routine use, enabling clinicians to access a patient's HIE record with a single click from within their electronic health record system. Access to integrated data and advanced search features supported clinical decision-making. Limited training and poor system usability were identified as barriers to use. CONCLUSIONS: Achieving widespread adoption and use of HIE systems globally will require a focused effort to address multiple individual perception and behavioral factors. Researchers, leaders of HIE organizations, and policymakers alike should leverage these factors to achieve the goals of HIE and interoperability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.032
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.161
GPT teacher head0.534
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Medical InformaticsSame topicElectronic Health Records SystemsFrench-language works237,207