Unhealthy alcohol use detection in electronic health records: A comparative study using natural language processing.
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| Názov: | Unhealthy alcohol use detection in electronic health records: A comparative study using natural language processing. |
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| Autori: | Ju X; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA., Solka J; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA., Weber K; Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA., Vydiswaran VV; Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA; School of Information, University of Michigan, Ann Arbor, MI, USA., Lin LA; Department 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., Bonar EE; Department 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., Fernandez AC; Department 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. |
| Zdroj: | Drug and alcohol dependence [Drug Alcohol Depend] 2025 Dec 01; Vol. 277, pp. 112920. Date of Electronic Publication: 2025 Oct 10. |
| Spôsob vydávania: | Journal Article; Comparative Study |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Elsevier Country of Publication: Ireland NLM ID: 7513587 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0046 (Electronic) Linking ISSN: 03768716 NLM ISO Abbreviation: Drug Alcohol Depend Subsets: MEDLINE |
| Imprint Name(s): | Publication: Limerick : Elsevier Original Publication: Lausanne, Elsevier Sequoia. |
| Výrazy zo slovníka MeSH: | Natural Language Processing* , Electronic Health Records* , Alcoholism*/diagnosis , Alcoholism*/epidemiology , Alcohol Drinking*/epidemiology, Humans ; Female ; Male ; Middle Aged ; Adult ; Primary Health Care ; Prospective Studies ; Young Adult ; Aged |
| Abstrakt: | Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Background: Unhealthy 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. Methods: We 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. Results: In 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. Conclusions: NLP 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. (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Alcohol use; Alcohol use disorder; Artificial intelligence; Electronic health records; Emerging technologies; Natural language processing |
| Entry Date(s): | Date Created: 20251018 Date Completed: 20251129 Latest Revision: 20251129 |
| Update Code: | 20251130 |
| DOI: | 10.1016/j.drugalcdep.2025.112920 |
| PMID: | 41109081 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Background: Unhealthy 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.<br />Methods: We 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.<br />Results: In 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.<br />Conclusions: NLP 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.<br /> (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.) |
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| ISSN: | 1879-0046 |
| DOI: | 10.1016/j.drugalcdep.2025.112920 |