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Last Posted: Jun 06, 2023
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Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program.
M Daniel Brannock et al. Nat Commun 2023 5 (1) 2914

We used electronic health records available through the National COVID Cohort Collaborative to characterize the association between SARS-CoV-2 vaccination and long COVID diagnosis. We found that vaccination was consistently associated with lower odds and rates of long COVID clinical diagnosis and high-confidence computationally derived diagnosis after adjusting for sex, demographics, and medical history.

Essential Electronic Health Record Reforms for This Decade.
Don Eugene Detmer et al. JAMA 2023 5

Few health care innovations have been more intrusive and ubiquitous than electronic health records (EHRs). Despite EHRs’ distinct advantages, the structure of health care services in the US has made it difficult to exploit their most desirable features. Instead of supporting clinicians seeking to deliver care more effectively and efficiently, current EHR design and configurations attempt to manage clinicians and how they do their work.

AI-assisted prediction of differential response to antidepressant classes using electronic health records.
Yi-Han Sheu et al. NPJ Digit Med 2023 4 (1) 73

Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.

Foundation models for generalist medical artificial intelligence
M Moor et al, Nature, April 12, 2023

We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities.

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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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