To evaluate whether increasing agentic architectural complexity via self-reflection or multiagent design improves clinical code status extraction (Do Not Resuscitate [DNR], Do Not Intubate [DNI], Comfort Measures Only [CMO]) across open-source large language models (LLMs) of varying capabilities.
Author(s): Sun, Yan, Ang, Gary, Yap, Chun Wei, Kannapiran, Palvannan, Abisheganaden, John
DOI: 10.1093/jamia/ocag179
To systematically review and characterize methodological heterogeneity in sepsis case detection using the Medical Information Mart for Intensive Care III (MIMIC-III) and eICU-CRD databases.
Author(s): Meyer-Eschenbach, Falk, Schmiedler, René, von Stoephasius, Johannes, Zhang, Camille, Kronfli, Luis, Frey, Nicolas, Näher, Anatol-Fiete, Ehret, Jacques, Nothacker, Johanna, von Kalle, Christof, Kohler, Severin, Grünewald, Elias, Edel, Andreas, Kumpf, Oliver, Barrenetxea, Jon, Balzer, Felix
DOI: 10.1093/jamia/ocag144
Smart healthcare innovations driven by Virtual Human Twin (VHT) technology enable continuous patient monitoring. However, standards to assess their stage of development or a unified framework for performance comparisons are still lacking. This study aims to quantitatively analyze VHT-related studies, evaluate their development levels, and align them to the VHT roadmap.
Author(s): Krishnamurthi, Vaishnavi, Edirisinghe, Ruwini, John, Sam E, Wickramasinghe, Nilmini, Plebanski, Magdalena
DOI: 10.1093/jamia/ocag174
Ovarian cancer (OvCa) patients and caregivers face complex, evolving information needs, yet digital health platforms often lack tailoring. This study developed and validated computational personas to support a tailored recommender system (Health E-Librarian with Personalized Recommendations [HELPeR]) and address the cold-start problem, in which limited information about new users reduces the system's ability to generate relevant recommendations.
Author(s): Wang, Youjia, Thaker, Khushboo, Hui, Vivian, Brusilovsky, Peter, He, Daqing, Donovan, Heidi, Lee, Young Ji
DOI: 10.1093/jamia/ocag169
To investigate the longitudinal effects of an ambient artificial intelligence (AI) scribe use in primary care on outcomes related to documentation time, note writing patterns, and clinician financial productivity.
Author(s): Waken, R J, Lou, Sunny S, Hofford, Mackenzie, Eiden, Elise, Burk, Carrie, Kim, Seunghwan, Esker, Jared, Zhang, Linying, Maddox, Thomas M, Abraham, Joanna, Lai, Albert M, Bhayani, Sam, O'Dell, Deborah, Schmidt, Jennifer, Paynter, Kayla, Thomas, Michele, Gerling, Matthew, Payne, Philip R O, Kannampallil, Thomas
DOI: 10.1093/jamia/ocag151
Interpretive clinical artificial intelligence (AI), in which a clinician interprets an image or signal and an AI system offers a competing or confirming read, is increasingly routine in radiology, pathology, dermatology, and endoscopy. Oversight of these systems assumes that independent human judgment occurred before AI influence entered the decision. The informatics infrastructure, meaning the electronic health record, its audit logs, and the reporting systems built on them, does not verify [...]
Author(s): Thornton, James, Henderson, Joshua M
DOI: 10.1093/jamia/ocag152
Clinician trust in artificial intelligence (AI) is a frequently cited barrier to adoption, yet there is limited empirical evidence on how transparency features influence trust. This study aimed to address this evidence gap.
Author(s): Frank, Cassiopeia, Stevens, Alexander, Hsu, Yea-Jen, Gao, Gordon, Streiff, Michael B, Engineer, Lilly
DOI: 10.1093/jamia/ocag176