Correction to: De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.
Author(s):
DOI: 10.1093/jamia/ocae154
Author(s):
DOI: 10.1093/jamia/ocae154
To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API).
Author(s): McMurry, Andrew J, Gottlieb, Daniel I, Miller, Timothy A, Jones, James R, Atreja, Ashish, Crago, Jennifer, Desai, Pankaja M, Dixon, Brian E, Garber, Matthew, Ignatov, Vladimir, Kirchner, Lyndsey A, Payne, Philip R O, Saldanha, Anil J, Shankar, Prabhu R V, Solad, Yauheni V, Sprouse, Elizabeth A, Terry, Michael, Wilcox, Adam B, Mandl, Kenneth D
DOI: 10.1093/jamia/ocae130
Clinical decision support systems (CDSSs) are computer applications, which can be applied to give guidance to practitioners in antimicrobial stewardship (AS) activities; however, further information is needed for their optimal use.
Author(s): Amor-García, Miguel Ángel, Chamorro-de-Vega, Esther, Rodríguez-González, Carmen Guadalupe, Iglesias-Peinado, Irene, Moreno-Díaz, Raquel
DOI: 10.1055/a-2341-8823
Author(s): Yan, Adam P, Yarahuan, Julia, Hron, Jonathan D
DOI: 10.1055/a-2340-7142
The method of documentation during a clinical encounter may affect the patient-physician relationship.
Author(s): Owens, Lance M, Wilda, J Joshua, Grifka, Ronald, Westendorp, Joan, Fletcher, Jeffrey J
DOI: 10.1055/a-2337-4739
The integration of large language models (LLMs) like ChatGPT into medical education presents potential benefits and challenges. These technologies, aligned with constructivist learning theories, could potentially enhance critical thinking and problem-solving through inquiry-based learning environments. However, the actual impact on educational outcomes and the effectiveness of these tools in fostering learning require further empirical study. This technological shift necessitates a reevaluation of curriculum design and the development of new assessment [...]
Author(s): Lawson McLean, Aaron
DOI: 10.1093/jamia/ocae124
To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data.
Author(s): Carrell, David S, Floyd, James S, Gruber, Susan, Hazlehurst, Brian L, Heagerty, Patrick J, Nelson, Jennifer C, Williamson, Brian D, Ball, Robert
DOI: 10.1093/jamia/ocae121
Author(s): Albiñana, Carlos Berenguer, Pallocca, Matteo, Fenton, Hayley, Sopwith, Will, Eden, Charlie Van, Akre, Olof, Auranen, Annika, Bocquet, François, Borges, Marina, Calvo, Emiliano, Corkett, John, Di Cosimo, Serena, Gentili, Nicola, Guérin, Julien, Jor, Sissel, Kazda, Tomas, Kolar, Alenka, Kuschel, Tim, Lostes, Maria Julia, Paratore, Chiara, Pedrazzoli, Paolo, Petrovic, Marko, Raid, Jarno, Roche, Miriam, Schatz, Christoph, Thonnard, Joelle, Tonon, Giovanni, Traverso, Alberto, Wolf, Andrea, Zedan, Ahmed H, Mahon, Piers
DOI: 10.1055/s-0044-1792139
The Accreditation Council for Graduate Medical Education suggests that Clinical Informatics (CI) fellowship programs foster broad skills, which include collaboration and project management. However, they do not dictate how to best accomplish these learning objectives.
Author(s): Leu, Michael G, Singh, Angad P, Lewis, Christopher W, Jane Fellner, B, Kim, Theresa B, Lin, Yu-Hsiang, Sutton, Paul R, White, Andrew A, Tarczy-Hornoch, Peter
DOI: 10.1055/s-0044-1788980
Variability in cardiopulmonary arrest training and management leads to inconsistent outcomes during in-hospital cardiac arrest. Existing clinical decision aids, such as American Heart Association (AHA) advanced cardiovascular life support (ACLS) pocket cards and third-party mobile apps, often lack comprehensive management guidance. We developed a novel, guided ACLS mobile app and evaluated user performance during simulated cardiac arrest according to the 2020 AHA ACLS guidelines via randomized controlled trial.
Author(s): Senter-Zapata, Michael, Neel, Dylan V, Colocci, Isabella, Alblooshi, Afaf, AlRadini, Faten Abdullah M, Quach, Brian, Lyon, Samuel, Coll, Maxwell, Chu, Andrew, Rainer, Katharine W, Waters, Beth, Baugh, Christopher W, Dias, Roger D, Zhang, Haipeng, Eyre, Andrew, Isselbacher, Eric, Conley, Jared, Carlile, Narath
DOI: 10.1055/s-0044-1788979