[This corrects the article DOI: 10.1093/jamiaopen/ooae003.].
Author(s):
DOI: 10.1093/jamiaopen/ooae030
A data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons (PRC) is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic.
Author(s): Trunnell, Matthew, Frankenberger, Casey, Hota, Bala, Hughes, Troy, Martinov, Plamen, Ravichandran, Urmila, Shah, Nirav S, Grossman, Robert L, ,
DOI: 10.1093/jamiaopen/ooae025
This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces.
Author(s): Kim, KangHyun, Kim, Sung-Min, Park, YoungMin, Lee, EunSol, Jung, SungJae, Kang, Jeongyong, An, DongUk, Min, Kyungil, Shim, Sung Ryul, Yu, Hyeong Won, Han, Hyun Wook
DOI: 10.1093/jamiaopen/ooae029
Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed.
Author(s): Baxter, Sally L, Longhurst, Christopher A, Millen, Marlene, Sitapati, Amy M, Tai-Seale, Ming
DOI: 10.1093/jamiaopen/ooae028
To evaluate patient-reported experiences of telehealth and disparities in access, use, and satisfaction with telehealth during the COVID-19 pandemic.
Author(s): Yoon, Esther, Hur, Scott, Curtis, Laura M, Benavente, Julia Yoshino, Wolf, Michael S, Serper, Marina
DOI: 10.1093/jamiaopen/ooae026
We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers.
Author(s): Barata, Filipe, Shim, Jinjoo, Wu, Fan, Langer, Patrick, Fleisch, Elgar
DOI: 10.1093/jamiaopen/ooae027
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocae126
Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label [...]
Author(s): Wei, Yishu, Deng, Yu, Sun, Cong, Lin, Mingquan, Jiang, Hongmei, Peng, Yifan
DOI: 10.1093/jamia/ocae108
Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpretable AI system and provide some valuable insights regarding short, and long-term mortality.
Author(s): Kim, Minwook, Kang, Donggil, Kim, Min Sun, Choe, Jeong Cheon, Lee, Sun-Hack, Ahn, Jin Hee, Oh, Jun-Hyok, Choi, Jung Hyun, Lee, Han Cheol, Cha, Kwang Soo, Jang, Kyungtae, Bong, WooR I, Song, Giltae, Lee, Hyewon
DOI: 10.1093/jamia/ocae114
We sought to (1) characterize the process of diagnosing pneumonia in an emergency department (ED) and (2) examine clinician reactions to a clinician-facing diagnostic discordance feedback tool.
Author(s): Butler, Jorie M, Taft, Teresa, Taber, Peter, Rutter, Elizabeth, Fix, Megan, Baker, Alden, Weir, Charlene, Nevers, McKenna, Classen, David, Cosby, Karen, Jones, Makoto, Chapman, Alec, Jones, Barbara E
DOI: 10.1093/jamia/ocae112