This article explores the potential of large language models (LLMs) to automate administrative tasks in healthcare, alleviating the burden on clinicians caused by electronic medical records.
Author(s): Tripathi, Satvik, Sukumaran, Rithvik, Cook, Tessa S
DOI: 10.1093/jamia/ocad258
Racial disparities in kidney transplant access and posttransplant outcomes exist between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in the United States, with the site of care being a key contributor. Using multi-site data to examine the effect of site of care on racial disparities, the key challenge is the dilemma in sharing patient-level data due to regulations for protecting patients' privacy.
Author(s): Tong, Jiayi, Shen, Yishan, Xu, Alice, He, Xing, Luo, Chongliang, Edmondson, Mackenzie, Zhang, Dazheng, Lu, Yiwen, Yan, Chao, Li, Ruowang, Siegel, Lianne, Sun, Lichao, Shenkman, Elizabeth A, Morton, Sally C, Malin, Bradley A, Bian, Jiang, Asch, David A, Chen, Yong
DOI: 10.1093/jamia/ocae075
Obtain clinicians' perspectives on early warning scores (EWS) use within context of clinical cases.
Author(s): Payne, Velma L, Sattar, Usman, Wright, Melanie, Hill, Elijah, Butler, Jorie M, Macpherson, Brekk, Jeppesen, Amanda, Del Fiol, Guilherme, Madaras-Kelly, Karl
DOI: 10.1093/jamia/ocae089
To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations.
Author(s): Iqbal, Usman, Lee, Leon Tsung-Ju, Rahmanti, Annisa Ristya, Celi, Leo Anthony, Li, Yu-Chuan Jack
DOI: 10.1093/jamia/ocae067
To develop and validate a natural language processing (NLP) pipeline that detects 18 conditions in French clinical notes, including 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-enhancing workflow.
Author(s): Petit-Jean, Thomas, Gérardin, Christel, Berthelot, Emmanuelle, Chatellier, Gilles, Frank, Marie, Tannier, Xavier, Kempf, Emmanuelle, Bey, Romain
DOI: 10.1093/jamia/ocae069
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DOI: 10.1093/jamia/ocae048
Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. However, this typically requires datasets with clinically confirmed labels collected in standardized environments, which is challenging, especially for a large subject pool. This study validates the efficacy of a self-supervised learning method in reducing the reliance on [...]
Author(s): Tripathi, Shikha, Acien, Alejandro, Holmes, Ashley A, Arroyo-Gallego, Teresa, Giancardo, Luca
DOI: 10.1093/jamia/ocae050
To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.
Author(s): Liu, Siru, McCoy, Allison B, Wright, Aileen P, Nelson, Scott D, Huang, Sean S, Ahmad, Hasan B, Carro, Sabrina E, Franklin, Jacob, Brogan, James, Wright, Adam
DOI: 10.1093/jamia/ocae041
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DOI: 10.1093/jamia/ocae083
Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios.
Author(s): Dong, Xin, Zhao, Chenxi, Song, Xinpeng, Zhang, Lei, Liu, Yu, Wu, Jun, Xu, Yiran, Xu, Ning, Liu, Jialing, Yu, Haibin, Yang, Kuo, Zhou, Xuezhong
DOI: 10.1093/jamia/ocae066