Diversity, equity, and inclusion matter for biomedical and health informatics.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocaf057
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocaf057
To measure hospital engagement in interoperable exchange of health-related social needs (HRSN) data.
Author(s): Sandhu, Sahil, Liu, Michael, Gottlieb, Laura M, Holmgren, A Jay, Rotenstein, Lisa S, Pantell, Matthew S
DOI: 10.1093/jamia/ocaf049
Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using existing approaches. This study aims to develop and evaluate a Large Language Model (LLM)-based framework for detecting PEAE from unstructured narrative data in EMRs.
Author(s): Cheligeer, Cheligeer, Southern, Danielle A, Yan, Jun, Wu, Guosong, Pan, Jie, Lee, Seungwon, Martin, Elliot A, Jafarpour, Hamed, Eastwood, Cathy A, Zeng, Yong, Quan, Hude
DOI: 10.1093/jamia/ocaf048
Although biomedical informatics has multiple roles to play in addressing the climate crisis, collaborative action and research agendas have yet to be developed. As a first step, AMIA's new Climate, Health, and Informatics Working Group held a mini-summit entitled Climate and health: How can informatics help? during the AMIA 2023 Fall Symposium to define an initial set of areas of interest and begin mobilizing informaticians to confront the urgent challenges [...]
Author(s): Schleyer, Titus, Berenji, Manijeh, Deck, Monica, Chung, Hana, Choi, Joshua, Cullen, Theresa A, Burdick, Timothy, Zaleski, Amanda, Craig, Kelly Jean Thomas, Fayanju, Oluseyi, Islam, Muhammad Muinul
DOI: 10.1093/jamia/ocae292
This article describes the challenges faced by the National Library of Medicine with the rise of artificial intelligence (AI) and access to human knowledge through large language models (LLMs).
Author(s): Lenert, Leslie Andrew
DOI: 10.1093/jamia/ocaf041
To determine the extent to which current large language models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors that can impact their adoption, including overall performance, calibration, fairness, and resilience to privacy protections that reduce data fidelity.
Author(s): Brown, Katherine E, Yan, Chao, Li, Zhuohang, Zhang, Xinmeng, Collins, Benjamin X, Chen, You, Clayton, Ellen Wright, Kantarcioglu, Murat, Vorobeychik, Yevgeniy, Malin, Bradley A
DOI: 10.1093/jamia/ocaf038
Although efforts to effectively govern AI continue to develop, relatively little work has been done to systematically measure and include patient perspectives or expectations of AI in governance. This analysis is designed to understand patient expectations of healthcare AI.
Author(s): Nong, Paige, Ji, Molin
DOI: 10.1093/jamia/ocaf031
Despite the proven usefulness of appropriate clinical decision support (CDS) alerts, many CDS systems fire excessive, clinically irrelevant alerts that are often ignored by clinicians. We have developed a method to suppress false-positive alerts based on prior drug tolerance but encountered substantial barriers to integrating the method into widely adopted commercial electronic health record (EHR) systems.This study aimed to describe the challenges faced while attempting to integrate our method into [...]
Author(s): Colicchio, Tiago K, ElHalta, David, Fiol, Guilherme Del, Kawamoto, Kensaku, Strasberg, Howard R, Cimino, James J
DOI: 10.1055/a-2546-5954
Author(s):
DOI: 10.1093/jamia/ocaf028
Digital health equity, the opportunity for all to engage with digital health tools to support good health outcomes, is an emerging priority across the world. The field of digital health equity would benefit from a comprehensive and systematic understanding of digital health, digital equity, and health equity, with a focus on real-world applications. We conducted a scoping review to identify and describe published frameworks and concepts relevant to digital health [...]
Author(s): Kim, Katherine K, Backonja, Uba
DOI: 10.1093/jamia/ocaf017