To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).
Author(s): Jackson, Brian R, Sendak, Mark P, Solomonides, Anthony, Balu, Suresh, Sittig, Dean F
DOI: 10.1093/jamia/ocae296
Author(s):
DOI: 10.1093/jamia/ocae309
The study aimed to assess the usage and impact of a private and secure instance of a generative artificial intelligence (GenAI) application in a large academic health center. The goal was to understand how employees interact with this technology and the influence on their perception of skill and work performance.
Author(s): Malhotra, Kiran, Wiesenfeld, Batia, Major, Vincent J, Grover, Himanshu, Aphinyanaphongs, Yindalon, Testa, Paul, Austrian, Jonathan S
DOI: 10.1093/jamia/ocae285
Our study aimed to expedite data sharing requests of Limited Data Sets (LDS) through the development of a streamlined platform that allows distributed, immutable management of network activities, provides transparent and intuitive auditing of data access history, and systematically evaluated it on a multi-capacity network setting for meaningful efficiency metrics.
Author(s): Yu, Yufei, Edelson, Maxim, Pham, Anh, Pekar, Jonathan E, Johnson, Brian, Post, Kai, Kuo, Tsung-Ting
DOI: 10.1093/jamia/ocae288
To determine the efficacy of the mLab App, a mobile-delivered HIV prevention intervention to increase HIV self-testing in MSM and TGW.
Author(s): Schnall, Rebecca, Scherr, Thomas Foster, Kuhns, Lisa M, Janulis, Patrick, Jia, Haomiao, Wood, Olivia R, Almodovar, Michael, Garofalo, Robert
DOI: 10.1093/jamia/ocae261
To be usable, useful, and sustainable for families of children with medically complex conditions (CMC), digital interventions must account for the complex sociotechnical context in which these families provide care. CMC experience higher neighborhood socioeconomic disadvantage than other child populations, which has associations with CMC health. Neighborhoods may influence the structure and function of the array of caregivers CMC depend upon (ie, the caregiving network).
Author(s): Werner, Nicole E, Morgen, Makenzie, Jolliff, Anna, Kieren, Madeline, Thomson, Joanna, Callahan, Scott, deJong, Neal, Foster, Carolyn, Ming, David, Randolph, Arielle, Stille, Christopher J, Ehlenbach, Mary, Katz, Barbara, Coller, Ryan J
DOI: 10.1093/jamiaopen/ooaf011
To develop and validate a machine learning model that helps physician advisors efficiently identify hospital admission denials likely to be overturned on appeal.
Author(s): Owolabi, Timothy
DOI: 10.1093/jamiaopen/ooaf016
This study evaluates the impact of an ambient artificial intelligence (AI) documentation platform on clinicians' perceptions of documentation workflow.
Author(s): Albrecht, Michael, Shanks, Denton, Shah, Tina, Hudson, Taina, Thompson, Jeffrey, Filardi, Tanya, Wright, Kelli, Ator, Gregory A, Smith, Timothy Ryan
DOI: 10.1093/jamiaopen/ooaf013
The predictive intensive care unit (ICU) scoring system is crucial for predicting patient outcomes, particularly mortality. Traditional scoring systems rely mainly on structured clinical data from electronic health records, which can overlook important clinical information in narratives and images.
Author(s): Lin, Mingquan, Wang, Song, Ding, Ying, Zhao, Lihui, Wang, Fei, Peng, Yifan
DOI: 10.1093/jamiaopen/ooae137
To enhance the accuracy of information retrieval from pharmacovigilance (PV) databases by employing Large Language Models (LLMs) to convert natural language queries (NLQs) into Structured Query Language (SQL) queries, leveraging a business context document.
Author(s): Painter, Jeffery L, Chalamalasetti, Venkateswara Rao, Kassekert, Raymond, Bate, Andrew
DOI: 10.1093/jamiaopen/ooaf003