Evidence map›Paper›PMID 41109081›Full record

ArticleDrug and alcohol dependence2025

Unhealthy alcohol use detection in electronic health records: A comparative study using natural language processing.

Xintong Ju, Jake Solka, Katherine Weber, Vg Vinod Vydiswaran, Lewei Allison Lin, Erin E Bonar, Anne C Fernandez

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Drug and alcohol dependence, 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

7 authors.

Xintong JuDepartment of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
Jake SolkaDepartment of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
Katherine WeberDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA.
Vg Vinod VydiswaranDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA; School of Information, University of Michigan, Ann Arbor, MI, USA.
Lewei Allison LinDepartment of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Michigan Innovations in Addiction Care through Research & Education (MI-ACRE), Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Addiction Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA.
Erin E BonarDepartment of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Michigan Innovations in Addiction Care through Research & Education (MI-ACRE), Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Addiction Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
Anne C FernandezDepartment of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Michigan Innovations in Addiction Care through Research & Education (MI-ACRE), Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Addiction Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA. Electronic address: acfernan@umich.edu.

Funding

Harnessing telemedicine to improve alcohol use disorder outcomes in primary care patientsR01AA029400 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BONAR, ERIN E., LIN, LEWEI ALLISON · 2021 to 2025
$3.3M
Leveraging electronic health records to identify risky alcohol use prior to surgeryR33AA028315 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FERNANDEZ, ANNE CHRISTIE, VYDISWARAN, VG VINOD · 2022 to 2024
$1.1M
Leveraging electronic health records to identify risky alcohol use prior to surgeryR21AA028315 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FERNANDEZ, ANNE CHRISTIE, VYDISWARAN, VG VINOD · 2020 to 2021
$398k
NIAAA NIH HHS R01 AA029400NIAAA NIH HHS R21 AA028315NIAAA NIH HHS R33 AA028315
6 · The paper itself

Abstract

backgroundUnhealthy alcohol use, including risky alcohol use and alcohol use disorder (AUD), are under-identified in primary care settings. Natural Language Processing (NLP) is a promising approach that could identify unhealthy alcohol use from clinical notes even when structured data (SD) indicators are lacking. This study prospectively evaluated the performance of SD and NLP in identifying unhealthy alcohol use in primary care patients.

methodsWe extracted electronic health record (EHR) data of primary care patients at a large Midwestern Health System (N = 133,144) and applied two identification approaches; an SD approach (i.e., diagnostic codes and alcohol screening scores) and an NLP-based approach. We then recruited N = 170 participants identified by SD (N = 85) or NLP (N = 85) to complete gold-standard self-report measures and compared the number of positive cases identified by each method.

resultsIn the full EHR sample, SD identified 820 cases of unhealthy alcohol use, and NLP identified 48,262 cases with unhealthy alcohol use. Among participants identified by SD, 41.18 % reported AUD, and 28.82 % reported risky alcohol use. Among those identified by NLP, 20 % reported AUD and 27.06 % reported risky alcohol use. Participants identified by SD had more AUD symptoms and mental health difficulties.

conclusionsNLP identified many primary care patients with indicators of unhealthy alcohol use that SD missed, indicating NLP could substantially expand identification of unhealthy alcohol use in primary care populations, particularly those with lower severity alcohol use disorder. NLP could complement traditional screening methods for comprehensive unhealthy alcohol use detection.

Indexed as

Alcohol DrinkingAlcoholismElectronic Health RecordsNatural Language ProcessingAdultAgedFemaleHumansMaleMiddle AgedPrimary Health CareProspective StudiesYoung AdultAlcohol useAlcohol use disorderArtificial intelligenceElectronic health recordsEmerging technologiesNatural language processing

Identifiers

PMID41109081
PMCPMC12681214

What OpenQuestion holds

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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.