Enhancing Postmarketing Surveillance of Medical Products With Large Language Models.

Autor:	Matheny ME; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.; Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.; Geriatric Research Education and Clinical Care Service, Tennessee Valley Healthcare System VA, Nashville., Yang J; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts., Smith JC; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee., Walsh CG; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.; Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.; Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee., Al-Garadi MA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee., Davis SE; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee., Marsolo KA; Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina., Fabbri D; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.; Department of Computer Science, Vanderbilt University, Nashville, Tennessee., Reeves RR; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.; Geriatric Research Education and Clinical Care Service, Tennessee Valley Healthcare System VA, Nashville., Johnson KB; Department of Epidemiology and Informatics, University of Pennsylvania, Philadelphia.; Department of Pediatrics, University of Pennsylvania, Philadelphia., Dal Pan GJ; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland., Ball R; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland., Desai RJ; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Jazyk:	angličtina
Zdroj:	JAMA network open [JAMA Netw Open] 2024 Aug 01; Vol. 7 (8), pp. e2428276. Date of Electronic Publication: 2024 Aug 01.
DOI:	10.1001/jamanetworkopen.2024.28276
Abstrakt:	Importance: The Sentinel System is a key component of the US Food and Drug Administration (FDA) postmarketing safety surveillance commitment and uses clinical health care data to conduct analyses to inform drug labeling and safety communications, FDA advisory committee meetings, and other regulatory decisions. However, observational data are frequently deemed insufficient for reliable evaluation of safety concerns owing to limitations in underlying data or methodology. Advances in large language models (LLMs) provide new opportunities to address some of these limitations. However, careful consideration is necessary for how and where LLMs can be effectively deployed for these purposes. Observations: LLMs may provide new avenues to support signal-identification activities to identify novel adverse event signals from narrative text of electronic health records. These algorithms may be used to support epidemiologic investigations examining the causal relationship between exposure to a medical product and an adverse event through development of probabilistic phenotyping of health outcomes of interest and extraction of information related to important confounding factors. LLMs may perform like traditional natural language processing tools by annotating text with controlled vocabularies with additional tailored training activities. LLMs offer opportunities for enhancing information extraction from adverse event reports, medical literature, and other biomedical knowledge sources. There are several challenges that must be considered when leveraging LLMs for postmarket surveillance. Prompt engineering is needed to ensure that LLM-extracted associations are accurate and specific. LLMs require extensive infrastructure to use, which many health care systems lack, and this can impact diversity, equity, and inclusion, and result in obscuring significant adverse event patterns in some populations. LLMs are known to generate nonfactual statements, which could lead to false positive signals and downstream evaluation activities by the FDA and other entities, incurring substantial cost. Conclusions and Relevance: LLMs represent a novel paradigm that may facilitate generation of information to support medical product postmarket surveillance activities that have not been possible. However, additional work is required to ensure LLMs can be used in a fair and equitable manner, minimize false positive findings, and support the necessary rigor of signal detection needed for regulatory activities.
Databáze:	MEDLINE
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