Correction to: Survey of clinical informatics fellows graduating 2016-2024: experiences before and during fellowship.
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DOI: 10.1093/jamia/ocad192
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DOI: 10.1093/jamia/ocad192
Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric prediction models. The primary objective was to determine whether a self-supervised model trained in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients, for pediatric inpatient clinical prediction tasks.
Author(s): Lemmon, Joshua, Guo, Lin Lawrence, Steinberg, Ethan, Morse, Keith E, Fleming, Scott Lanyon, Aftandilian, Catherine, Pfohl, Stephen R, Posada, Jose D, Shah, Nigam, Fries, Jason, Sung, Lillian
DOI: 10.1093/jamia/ocad175
This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed.
Author(s): Coiera, Enrico
DOI: 10.1093/jamia/ocad176
To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes.
Author(s): Carrasco-Ribelles, Lucía A, Llanes-Jurado, José, Gallego-Moll, Carlos, Cabrera-Bean, Margarita, Monteagudo-Zaragoza, Mònica, Violán, Concepción, Zabaleta-Del-Olmo, Edurne
DOI: 10.1093/jamia/ocad168
Author(s): Abbasi, Kamran, Ali, Parveen, Barbour, Virginia, Benfield, Thomas, Bibbins-Domingo, Kirsten, Hancocks, Stephen, Horton, Richard, Laybourn-Langton, Laurie, Mash, Robert, Sahni, Peush, Sharief, Wadeia Mohammad, Yonga, Paul, Zielinski, Chris
DOI: 10.1093/jamia/ocad206
Climate change, an underlying risk driver of natural disasters, threatens the environmental sustainability, planetary health, and sustainable development goals. Incorporating disaster-related health impacts into electronic health records helps to comprehend their impact on populations, clinicians, and healthcare systems. This study aims to: (1) map the United Nations Office for Disaster Risk Reduction and International Science Council (UNDRR-ISC) Hazard Information Profiles to SNOMED CT International, a clinical terminology used by clinicians [...]
Author(s): Lokmic-Tomkins, Zerina, Block, Lorraine J, Davies, Shauna, Reid, Lisa, Ronquillo, Charlene Esteban, von Gerich, Hanna, Peltonen, Laura-Maria
DOI: 10.1093/jamia/ocad153
To honor the legacy of nursing informatics pioneer and visionary, Dr. Virginia Saba, the Friends of the National Library of Medicine convened a group of international experts to reflect on Dr. Saba's contributions to nursing standardized nursing terminologies.
Author(s): Dunn Lopez, Karen, Heermann Langford, Laura, Kennedy, Rosemary, McCormick, Kathleen, Delaney, Connie White, Alexander, Greg, Englebright, Jane, Carroll, Whende M, Monsen, Karen A
DOI: 10.1093/jamia/ocad156
Standardized nursing terminologies (SNTs) are necessary to ensure consistent knowledge expression and compare the effectiveness of nursing practice across settings. This study investigated whether SNTs can support semantic interoperability and outcoming tracking over time by implementing an AI-powered CDS tool for fall prevention across multiple EMR systems.
Author(s): Cho, Insook, Cho, Jiseon, Hong, Jeong Hee, Choe, Wha Suk, Shin, HyeKyeong
DOI: 10.1093/jamia/ocad145
Theory-based research of social and behavioral determinants of health (SBDH) found SBDH-related patterns in interventions and outcomes for pregnant/birthing people. The objectives of this study were to replicate the theory-based SBDH study with a new sample, and to compare these findings to a data-driven SBDH study.
Author(s): Austin, Robin R, McLane, Tara M, Pieczkiewicz, David S, Adam, Terrence, Monsen, Karen A
DOI: 10.1093/jamia/ocad148