Correction to: Privacy-protecting, reliable response data discovery using COVID-19 patient observations.
Author(s):
DOI: 10.1093/jamia/ocad069
Author(s):
DOI: 10.1093/jamia/ocad069
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocad091
Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies.
Author(s): Alpi, Kristine M, Martin, Christie L, Plasek, Joseph M, Sittig, Scott, Smith, Catherine Arnott, Weinfurter, Elizabeth V, Wells, Jennifer K, Wong, Rachel, Austin, Robin R
DOI: 10.1093/jamia/ocad082
To describe the application of nudges within electronic health records (EHRs) and their effects on inpatient care delivery, and identify design features that support effective decision-making without the use of interruptive alerts.
Author(s): Raban, Magdalena Z, Gates, Peter J, Gamboa, Sarah, Gonzalez, Gabriela, Westbrook, Johanna I
DOI: 10.1093/jamia/ocad083
To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end clinical AI implementation framework.
Author(s): van der Vegt, Anton H, Scott, Ian A, Dermawan, Krishna, Schnetler, Rudolf J, Kalke, Vikrant R, Lane, Paul J
DOI: 10.1093/jamia/ocad075
The 21st Century Cures Act (Cures Act) information blocking regulations mandate timely patient access to their electronic health information. In most healthcare systems, this technically requires immediate electronic release of test results and clinical notes directly to patients. Patients could potentially be distressed by receiving upsetting results through an electronic portal rather than from a clinician. We present a case from 2018, several years prior to the implementation of the [...]
Author(s): Rotholz, Stephen, Lin, Chen-Tan
DOI: 10.1093/jamia/ocad074
To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions.
Author(s): Liu, Siru, Wright, Aileen P, Patterson, Barron L, Wanderer, Jonathan P, Turer, Robert W, Nelson, Scott D, McCoy, Allison B, Sittig, Dean F, Wright, Adam
DOI: 10.1093/jamia/ocad072
We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds.
Author(s): Campbell, Ian M, Karavite, Dean J, Mcmanus, Morgan L, Cusick, Fred C, Junod, David C, Sheppard, Sarah E, Lourie, Eli M, Shelov, Eric D, Hakonarson, Hakon, Luberti, Anthony A, Muthu, Naveen, Grundmeier, Robert W
DOI: 10.1093/jamia/ocad070
To examine the real-world safety problems involving machine learning (ML)-enabled medical devices.
Author(s): Lyell, David, Wang, Ying, Coiera, Enrico, Magrabi, Farah
DOI: 10.1093/jamia/ocad065
To design and validate a novel deep generative model for seismocardiogram (SCG) dataset augmentation. SCG is a noninvasively acquired cardiomechanical signal used in a wide range of cardivascular monitoring tasks; however, these approaches are limited due to the scarcity of SCG data.
Author(s): Nikbakht, Mohammad, Gazi, Asim H, Zia, Jonathan, An, Sungtae, Lin, David J, Inan, Omer T, Kamaleswaran, Rishikesan
DOI: 10.1093/jamia/ocad067