Evidence map›Paper›PMID 42468397›Full record

ArticleInternational journal of medical informatics2026

Bridging the trust-adoption gap for AI scribes in rural communities: A machine learning approach using the 2024 Canadian digital health survey.

Zhaoqiang Zhou, John Geracitano, Sandy Hatoum, Fei Yu, Saif Khairat

Abstract read
In one paragraph

Article in International journal of medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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.

Zhaoqiang ZhouCarolina Health Informatics Program, University of North Carolina at Chapel Hill, NC, USA; School of Data and Information Sciences, University of North Carolina at Chapel Hill, NC, USA.
John GeracitanoCarolina Health Informatics Program, University of North Carolina at Chapel Hill, NC, USA; School of Data and Information Sciences, University of North Carolina at Chapel Hill, NC, USA.
Sandy HatoumSchool of Nursing, University of North Carolina at Chapel Hill, NC, USA.
Fei YuCarolina Health Informatics Program, University of North Carolina at Chapel Hill, NC, USA; School of Data and Information Sciences, University of North Carolina at Chapel Hill, NC, USA.
Saif KhairatCarolina Health Informatics Program, University of North Carolina at Chapel Hill, NC, USA; School of Nursing, University of North Carolina at Chapel Hill, NC, USA; The Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: saif@unc.edu.

Funding

Center for Virtual Care Value and Excellence (ViVE). RC2TR004380 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Saif Khairat · 2023 to 2026
$3.0M
NCATS NIH HHS RC2 TR004380
6 · The paper itself

Abstract

backgroundAmbient AI scribe tools that capture clinician-patient conversations and generate draft notes are increasingly deployed to reduce documentation burden, but patient-facing acceptance may influence implementation success, especially in rural areas.

objectivesTo characterize rural respondents' attitudes toward AI scribes across (1) trust in documentation accuracy, (2) perceived impact on patient-provider interaction, and (3) preference for future use, and to examine how attitudes vary by demographic, socioeconomic, health, and digital-access characteristics.

methodsWe conducted a cross-sectional analysis of 1,050 rural respondents in the 2024 Canadian Digital Health Survey. Each outcome was dichotomized. XGBoost classifiers were trained for each outcome using prespecified predictors (sex, age group, race/ethnicity, education, employment status, household income, chronic disease, self-reported health, and high-speed internet access). Models demonstrated strong overall performance on a held-out test set. Subgroup differences were summarized using marginally standardized predicted probabilities with bootstrap 95 % confidence intervals.

resultsPredicted endorsement decreased across three attitude domains, from trust in documentation accuracy to interaction benefit and future-use preference. Predicted endorsement was higher among males than females across outcomes (e.g., future-use preference: 0.388 vs 0.313). Higher education and chronic disease were consistently associated with more favorable responses (e.g., future-use preference: graduate degree 0.466 vs less than high school 0.308; chronic disease 0.408 vs no condition 0.298). Compared with White respondents, visible minority, non-Indigenous respondents had lower future-use preference (0.293 vs 0.349), while Indigenous respondents showed higher predicted future-use preference (0.423 vs 0.349). Predicted probabilities were similar by internet access status across all three outcomes.

conclusionsAmong rural respondents, trust in AI scribe accuracy does not fully translate into perceived interaction benefit or willingness to use AI scribes in future encounters, supporting rollout strategies that prioritize clear communication, privacy transparency, and meaningful choice.

Indexed as

Artificial IntelligenceDocumentationElectronic Health RecordsMachine LearningRural PopulationTrustAdolescentAdultAgedCanadaCross-Sectional StudiesDigital HealthFemaleHumansMaleMiddle AgedAmbient intelligenceArtificial intelligenceDigital healthDocumentationRural population

Identifiers

PMID42468397
PMCPMC13502366

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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.