AI-Enabled EHR Mining of SBDOH Enables Diverse Trial Recruitment but Raises Ethical Concerns.
This article focuses on the challenges and ethical considerations of using artificial intelligence (AI) to mine electronic health records (EHR) for social and behavioral determinants of health (SBDOH) to support clinical trial recruitment. It highlights that SBDOH documentation in EHRs is often inco...
| Publicado en: | American Journal of Bioethics Vol. 26; no. 8; pp. 46 - 49 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Taylor & Francis Ltd
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195895398&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195895398 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15265161 FKZ jtl: American Journal of Bioethics issn: 15265161 maglogo: N pubinfo: dt: Aug2026 vid: 26 iid: 8 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 195895398 10.1080/15265161.2026.2690956 195895398 ppf: 46 ppct: 3 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AI-Enabled EHR Mining of SBDOH Enables Diverse Trial Recruitment but Raises Ethical Concerns. aug: au: Saylor, Katherine Witte Owens, Kellie affil: Geisinger College of Health Sciences sug: subj: Artificial Intelligence Electronic Health Records Social Determinants of Health Research Subject Recruitment Research Ethics Data Mining Natural Language Processing Privacy and Confidentiality Health Inequities Special Populations Consent Patient Participation Algorithms ab: This article focuses on the challenges and ethical considerations of using artificial intelligence (AI) to mine electronic health records (EHR) for social and behavioral determinants of health (SBDOH) to support clinical trial recruitment. It highlights that SBDOH documentation in EHRs is often incomplete, inconsistent, and scattered across structured and unstructured fields, limiting AI’s effectiveness and potentially introducing bias in recruitment pools. The article discusses concerns about privacy, patient consent, and the risk of stigmatization, emphasizing the need for transparency, patient choice, and empirical research on patient perspectives regarding AI use of sensitive SBDOH data. It concludes that addressing these issues requires ongoing public engagement, improved documentation practices, and ethical governance to ensure trust and fairness in AI-enabled research recruitment. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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