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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

Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study

Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study

To assess the information provided by the LLMs regarding the health conditions, we used scispa Cy (The Allen Institute for Artificial Intelligence) for named entity recognition, specifically, the en_core_sci_lg model in combination with the Unified Medical Language System vocabulary.

Michael S Deiner, Russell Y Deiner, Cherie Fathy, Natalie A Deiner, Vagelis Hristidis, Stephen D McLeod, Thomas J Bukowski, Thuy Doan, Gerami D Seitzman, Thomas M Lietman, Travis C Porco

J Med Internet Res 2025;27:e65226