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
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
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
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
Health organizations and systems rely on increasingly sophisticated informatics infrastructure. Without anti-racist expertise, the field risks reifying and entrenching racism in information systems. We consider ways the informatics field can recognize institutional, systemic, and structural racism and propose the use of the Public Health Critical Race Praxis (PHCRP) to mitigate and dismantle racism in digital forms. We enumerate guiding questions for stakeholders along with a PHCRP-Informatics framework. By focusing on [...]
Author(s): Platt, Jodyn, Nong, Paige, Merid, Beza, Raj, Minakshi, Cope, Elizabeth, Kardia, Sharon, Creary, Melissa
DOI: 10.1093/jamia/ocad123
Foundational domains are the building blocks of educational programs. The lack of foundational domains in undergraduate health informatics (HI) education can adversely affect the development of rigorous curricula and may impede the attainment of CAHIIM accreditation of academic programs.
Author(s): Khairat, Saif, Feldman, Sue S, Rana, Arif, Faysel, Mohammad, Purkayastha, Saptarshi, Scotch, Matthew, Eldredge, Christina
DOI: 10.1093/jamia/ocad147
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
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 assess large language models on their ability to accurately infer cancer disease response from free-text radiology reports.
Author(s): Tan, Ryan Shea Ying Cong, Lin, Qian, Low, Guat Hwa, Lin, Ruixi, Goh, Tzer Chew, Chang, Christopher Chu En, Lee, Fung Fung, Chan, Wei Yin, Tan, Wei Chong, Tey, Han Jieh, Leong, Fun Loon, Tan, Hong Qi, Nei, Wen Long, Chay, Wen Yee, Tai, David Wai Meng, Lai, Gillianne Geet Yi, Cheng, Lionel Tim-Ee, Wong, Fuh Yong, Chua, Matthew Chin Heng, Chua, Melvin Lee Kiang, Tan, Daniel Shao Weng, Thng, Choon Hua, Tan, Iain Bee Huat, Ng, Hwee Tou
DOI: 10.1093/jamia/ocad133
Clinical decision support (CDS) systems powered by predictive models have the potential to improve the accuracy and efficiency of clinical decision-making. However, without sufficient validation, these systems have the potential to mislead clinicians and harm patients. This is especially true for CDS systems used by opioid prescribers and dispensers, where a flawed prediction can directly harm patients. To prevent these harms, regulators and researchers have proposed guidance for validating predictive [...]
Author(s): McElfresh, Duncan C, Chen, Lucia, Oliva, Elizabeth, Joyce, Vilija, Rose, Sherri, Tamang, Suzanne
DOI: 10.1093/jamia/ocad110