Clickbusters letter response.
Author(s): McCoy, Allison B, Russo, Elise M, Wright, Adam
DOI: 10.1093/jamia/ocad150
Author(s): McCoy, Allison B, Russo, Elise M, Wright, Adam
DOI: 10.1093/jamia/ocad150
Author(s): Kannry, Joseph
DOI: 10.1093/jamia/ocad151
This article reports on the alignment between the foundational domains and the delineation of practice (DoP) for health informatics, both developed by the American Medical Informatics Association (AMIA). Whereas the foundational domains guide graduate-level curriculum development and accreditation assessment, providing an educational pathway to the minimum competencies needed as a health informatician, the DoP defines the domains, tasks, knowledge, and skills that a professional needs to competently perform in the [...]
Author(s): Johnson, Todd R, Berner, Eta S, Feldman, Sue S, Jones, Josette, Valenta, Annette L, Borbolla, Damian, Deckard, Gloria, Manos, LaVerne
DOI: 10.1093/jamia/ocad146
Rare disease research requires data sharing networks to power translational studies. We describe novel use of Research Electronic Data Capture (REDCap), a web application for managing clinical data, by the National Mesothelioma Virtual Bank, a federated biospecimen, and data sharing network.
Author(s): Rashid, Rumana, Copelli, Susan, Silverstein, Jonathan C, Becich, Michael J
DOI: 10.1093/jamia/ocad132
Patient-clinician communication provides valuable explicit and implicit information that may indicate adverse medical conditions and outcomes. However, practical and analytical approaches for audio-recording and analyzing this data stream remain underexplored. This study aimed to 1) analyze patients' and nurses' speech in audio-recorded verbal communication, and 2) develop machine learning (ML) classifiers to effectively differentiate between patient and nurse language.
Author(s): Zolnoori, Maryam, Vergez, Sasha, Sridharan, Sridevi, Zolnour, Ali, Bowles, Kathryn, Kostic, Zoran, Topaz, Maxim
DOI: 10.1093/jamia/ocad139
Physicians of all specialties experienced unprecedented stressors during the COVID-19 pandemic, exacerbating preexisting burnout. We examine burnout's association with perceived and actionable electronic health record (EHR) workload factors and personal, professional, and organizational characteristics with the goal of identifying levers that can be targeted to address burnout.
Author(s): Tai-Seale, Ming, Baxter, Sally, Millen, Marlene, Cheung, Michael, Zisook, Sidney, Çelebi, Julie, Polston, Gregory, Sun, Bryan, Gross, Erin, Helsten, Teresa, Rosen, Rebecca, Clay, Brian, Sinsky, Christine, Ziedonis, Douglas M, Longhurst, Christopher A, Savides, Thomas J
DOI: 10.1093/jamia/ocad136
To determine whether the Office of the National Coordinator's policy change restricting the use of "gag clauses" in contracts between electronic health record (EHR) vendors and healthcare facilities increased the prevalence of screenshots in peer-reviewed literature.
Author(s): Bapna, Monika, Miller, Kristen, Ratwani, Raj M
DOI: 10.1093/jamia/ocad138
Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population.
Author(s): Bhattarai, Kritib, Rajaganapathy, Sivaraman, Das, Trisha, Kim, Yejin, Chen, Yongbin, , , , , Dai, Qiying, Li, Xiaoyang, Jiang, Xiaoqian, Zong, Nansu
DOI: 10.1093/jamia/ocad135
Clinical decision support (CDS) can prevent medical errors and improve patient outcomes. Electronic health record (EHR)-based CDS, designed to facilitate prescription drug monitoring program (PDMP) review, has reduced inappropriate opioid prescribing. However, the pooled effectiveness of CDS has exhibited substantial heterogeneity and current literature does not adequately detail why certain CDS are more successful than others. Clinicians regularly override CDS, limiting its impact. No studies recommend how to help nonadopters [...]
Author(s): Sommers, Stuart, Tolle, Heather, Napier, Cheryl, Hoppe, Jason
DOI: 10.1093/jamia/ocad127
Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows.
Author(s): Chae, Sena, Davoudi, Anahita, Song, Jiyoun, Evans, Lauren, Hobensack, Mollie, Bowles, Kathryn H, McDonald, Margaret V, Barrón, Yolanda, Rossetti, Sarah Collins, Cato, Kenrick, Sridharan, Sridevi, Topaz, Maxim
DOI: 10.1093/jamia/ocad129