Correction to: Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices.
Author(s):
DOI: 10.1093/jamia/ocae068
Author(s):
DOI: 10.1093/jamia/ocae068
Genomic kidney conditions often have a long lag between onset of symptoms and diagnosis. To design a real time genetic diagnosis process that meets the needs of nephrologists, we need to understand the current state, barriers, and facilitators nephrologists and other clinicians who treat kidney conditions experience, and identify areas of opportunity for improvement and innovation.
Author(s): Romagnoli, Katrina M, Salvati, Zachary M, Johnson, Darren K, Ramey, Heather M, Chang, Alexander R, Williams, Marc S
DOI: 10.1093/jamia/ocae053
This study aimed to support the implementation of the 11th Revision of the International Classification of Diseases (ICD-11). We used common comorbidity indices as a case study for proactively assessing the impact of transitioning to ICD-11 for mortality and morbidity statistics (ICD-11-MMS) on real-world data analyses.
Author(s): Nikiema, Jean Noel, Thiam, Djeneba, Bayani, Azadeh, Ayotte, Alexandre, Sourial, Nadia, Bally, Michèle
DOI: 10.1093/jamia/ocae046
This article aims to examine how generative artificial intelligence (AI) can be adopted with the most value in health systems, in response to the Executive Order on AI.
Author(s): Jindal, Jenelle A, Lungren, Matthew P, Shah, Nigam H
DOI: 10.1093/jamia/ocae043
To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.
Author(s): Liu, Siru, McCoy, Allison B, Wright, Aileen P, Nelson, Scott D, Huang, Sean S, Ahmad, Hasan B, Carro, Sabrina E, Franklin, Jacob, Brogan, James, Wright, Adam
DOI: 10.1093/jamia/ocae041
Obtain clinicians' perspectives on early warning scores (EWS) use within context of clinical cases.
Author(s): Payne, Velma L, Sattar, Usman, Wright, Melanie, Hill, Elijah, Butler, Jorie M, Macpherson, Brekk, Jeppesen, Amanda, Del Fiol, Guilherme, Madaras-Kelly, Karl
DOI: 10.1093/jamia/ocae089
This study aims to facilitate the creation of quality standardized nursing statements in South Korea's hospitals using algorithmic generation based on the International Classifications of Nursing Practice (ICNP) and evaluation through Large Language Models.
Author(s): Kim, Hyeoneui, Park, Hyewon, Kang, Sunghoon, Kim, Jinsol, Kim, Jeongha, Jung, Jinsun, Taira, Ricky
DOI: 10.1093/jamia/ocae070
Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence.
Author(s): Leroy, Gondy, Andrews, Jennifer G, KeAlohi-Preece, Madison, Jaswani, Ajay, Song, Hyunju, Galindo, Maureen Kelly, Rice, Sydney A
DOI: 10.1093/jamia/ocae080
The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data.
Author(s): Gao, Jifan, Chen, Guanhua, O'Rourke, Ann P, Caskey, John, Carey, Kyle A, Oguss, Madeline, Stey, Anne, Dligach, Dmitriy, Miller, Timothy, Mayampurath, Anoop, Churpek, Matthew M, Afshar, Majid
DOI: 10.1093/jamia/ocae071
To develop and validate a natural language processing (NLP) pipeline that detects 18 conditions in French clinical notes, including 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-enhancing workflow.
Author(s): Petit-Jean, Thomas, Gérardin, Christel, Berthelot, Emmanuelle, Chatellier, Gilles, Frank, Marie, Tannier, Xavier, Kempf, Emmanuelle, Bey, Romain
DOI: 10.1093/jamia/ocae069