We aimed to develop a model for accurate prediction of general care inpatient deterioration.
Author(s): Romero-Brufau, Santiago, Whitford, Daniel, Johnson, Matthew G, Hickman, Joel, Morlan, Bruce W, Therneau, Terry, Naessens, James, Huddleston, Jeanne M
DOI: 10.1093/jamia/ocaa347
There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals).
Author(s): Rossetti, Sarah Collins, Knaplund, Chris, Albers, Dave, Dykes, Patricia C, Kang, Min Jeoung, Korach, Tom Z, Zhou, Li, Schnock, Kumiko, Garcia, Jose, Schwartz, Jessica, Fu, Li-Heng, Klann, Jeffrey G, Lowenthal, Graham, Cato, Kenrick
DOI: 10.1093/jamia/ocab006
Multimodal automated phenotyping (MAP) is a scalable, high-throughput phenotyping method, developed using electronic health record (EHR) data from an adult population. We tested transportability of MAP to a pediatric population.
Author(s): Geva, Alon, Liu, Molei, Panickan, Vidul A, Avillach, Paul, Cai, Tianxi, Mandl, Kenneth D
DOI: 10.1093/jamia/ocaa343
This study sought to describe gender representation in leadership and recognition within the U.S. biomedical informatics community.
Author(s): Griffin, Ashley C, Leung, Tiffany I, Tenenbaum, Jessica D, Chung, Arlene E
DOI: 10.1093/jamia/ocaa344
To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).
Author(s): Sarma, Karthik V, Harmon, Stephanie, Sanford, Thomas, Roth, Holger R, Xu, Ziyue, Tetreault, Jesse, Xu, Daguang, Flores, Mona G, Raman, Alex G, Kulkarni, Rushikesh, Wood, Bradford J, Choyke, Peter L, Priester, Alan M, Marks, Leonard S, Raman, Steven S, Enzmann, Dieter, Turkbey, Baris, Speier, William, Arnold, Corey W
DOI: 10.1093/jamia/ocaa341
Inaccurate surgical preference cards (supply lists) are associated with higher direct costs, waste, and delays. Numerous preference card improvement projects have relied on institution-specific, manual approaches of limited reproducibility. We developed and tested an algorithm to facilitate the first automated, informatics-based, fully reproducible approach.
Author(s): Scheinker, David, Hollingsworth, Matt, Brody, Anna, Phelps, Carey, Bryant, William, Pei, Francesca, Petersen, Kristin, Reddy, Alekhya, Wall, James
DOI: 10.1093/jamia/ocaa275
The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics.
Author(s): Roimi, Michael, Gutman, Rom, Somer, Jonathan, Ben Arie, Asaf, Calman, Ido, Bar-Lavie, Yaron, Gelbshtein, Udi, Liverant-Taub, Sigal, Ziv, Arnona, Eytan, Danny, Gorfine, Malka, Shalit, Uri
DOI: 10.1093/jamia/ocab005
Although nurses comprise the largest group of health professionals and electronic health record (EHR) user base, it is unclear how EHR use has affected nurse well-being. This systematic review assesses the multivariable (ie, organizational, nurse, and health information technology [IT]) factors associated with EHR-related nurse well-being and identifies potential improvements recommended by frontline nurses.
Author(s): Nguyen, Oliver T, Shah, Shivani, Gartland, Alexander J, Parekh, Arpan, Turner, Kea, Feldman, Sue S, Merlo, Lisa J
DOI: 10.1093/jamia/ocaa289
Artificial intelligence (AI) is increasingly being proposed for use in medicine, including breast cancer screening (BCS). Little is known, however, about referring primary care providers' (PCPs') preferences for this technology.
Author(s): Hendrix, Nathaniel, Hauber, Brett, Lee, Christoph I, Bansal, Aasthaa, Veenstra, David L
DOI: 10.1093/jamia/ocaa292
We aimed to assess associations of physician's work overload, successive work shifts, and work experience with physicians' risk to err.
Author(s): Leviatan, Ilona, Oberman, Bernice, Zimlichman, Eyal, Stein, Gideon Y
DOI: 10.1093/jamia/ocaa219