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Detecting Conversation Topics in Recruitment Calls of African American Participants to the All of Us Research Program Using Machine Learning: Model Development and Validation Study

Detecting Conversation Topics in Recruitment Calls of African American Participants to the All of Us Research Program Using Machine Learning: Model Development and Validation Study

Illustrative examples of talk turns in Topic 6 include instances of RAs “sending emails [with] instructions on how to register” for the research study, as well as “email[s] with the link to the [All of Us] website for more information and also a link to start an account if [the participant is] ready for that.” We also found many examples of RAs walking participants through challenges accessing their portals once created.

Priscilla Pemu, Michael Prude, Atuarra McCaslin, Elizabeth Ojemakinde, Christopher Awad, Kelechi Igwe, Anny Rodriguez, Jasmine Foriest, Muhammed Idris

JMIR Form Res 2025;9:e65320

Networked Behaviors Associated With a Large-Scale Secure Messaging Network: Cross-Sectional Secondary Data Analysis

Networked Behaviors Associated With a Large-Scale Secure Messaging Network: Cross-Sectional Secondary Data Analysis

This is also reflected in cluster 1’s increased message volume and messaging behavior, most likely highlighting their role in clinical decision-making. However, such a centralized communication structure may also increase physicians’ workload and cognitive burden arising from an increased messaging volume [57]. Similarly, there was one cluster (cluster 4) of nurses and medical assistants who had fewer connections and were not as central within the network.

Laura Rosa Baratta, Linlin Xia, Daphne Lew, Elise Eiden, Y Jasmine Wu, Noshir Contractor, Bruce L Lambert, Sunny S Lou, Thomas Kannampallil

JMIR Med Inform 2025;13:e66544

Improving the Readability of Institutional Heart Failure–Related Patient Education Materials Using GPT-4: Observational Study

Improving the Readability of Institutional Heart Failure–Related Patient Education Materials Using GPT-4: Observational Study

We aim to expand on the previous literature by assessing the readability of heart failure–related online PEMs from renowned cardiology institutions, assessing GPT-4’s ability to improve the readability of these PEMs, and comparing the accuracy and comprehensiveness between institutional PEMs and GPT-4’s revised PEMs.

Ryan C King, Jamil S Samaan, Joseph Haquang, Vishnu Bharani, Samuel Margolis, Nitin Srinivasan, Yuxin Peng, Yee Hui Yeo, Roxana Ghashghaei

JMIR Cardio 2025;9:e68817