[This retracts the article DOI: 10.1093/jamiaopen/ooad090.].
Author(s):
DOI: 10.1093/jamiaopen/ooae036
Decision support can improve shared decision-making for breast cancer treatment, but workflow barriers have hindered widespread use of these tools. The goal of this study was to understand the workflow among breast cancer teams of clinicians, patients, and their family caregivers when making treatment decisions and identify design guidelines for informatics tools to better support treatment decision-making.
Author(s): Salwei, Megan E, Reale, Carrie
DOI: 10.1093/jamiaopen/ooae053
To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms.
Author(s): Thayer, Daniel S, Mumtaz, Shahzad, Elmessary, Muhammad A, Scanlon, Ieuan, Zinnurov, Artur, Coldea, Alex-Ioan, Scanlon, Jack, Chapman, Martin, Curcin, Vasa, John, Ann, DelPozo-Banos, Marcos, Davies, Hannah, Karwath, Andreas, Gkoutos, Georgios V, Fitzpatrick, Natalie K, Quint, Jennifer K, Varma, Susheel, Milner, Chris, Oliveira, Carla, Parkinson, Helen, Denaxas, Spiros, Hemingway, Harry, Jefferson, Emily
DOI: 10.1093/jamiaopen/ooae049
Diagnosing rare diseases is an arduous and challenging process in clinical settings, resulting in the late discovery of novel variants and referral loops. To help clinicians, we built IDeRare pipelines to accelerate phenotype-genotype analysis for patients with suspected rare diseases.
Author(s): Harsono, Ivan William, Ariani, Yulia, Benyamin, Beben, Fadilah, Fadilah, Pujianto, Dwi Ari, Hafifah, Cut Nurul
DOI: 10.1093/jamiaopen/ooae052
The generation of structured documents for clinical trials is a promising application of large language models (LLMs). We share opportunities, insights, and challenges from a competitive challenge that used LLMs for automating clinical trial documentation.
Author(s): Landman, Rogier, Healey, Sean P, Loprinzo, Vittorio, Kochendoerfer, Ulrike, Winnier, Angela Russell, Henstock, Peter V, Lin, Wenyi, Chen, Aqiu, Rajendran, Arthi, Penshanwar, Sushant, Khan, Sheraz, Madhavan, Subha
DOI: 10.1093/jamiaopen/ooae043
[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].
Author(s):
DOI: 10.1093/jamiaopen/ooae046
Integrating clinical research into routine clinical care workflows within electronic health record systems (EHRs) can be challenging, expensive, and labor-intensive. This case study presents a large-scale clinical research project conducted entirely within a commercial EHR during the COVID-19 pandemic.
Author(s): Goldhaber, Nicole H, Jacobs, Marni B, Laurent, Louise C, Knight, Rob, Zhu, Wenhong, Pham, Dean, Tran, Allen, Patel, Sandip P, Hogarth, Michael, Longhurst, Christopher A
DOI: 10.1093/jamiaopen/ooae023
This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces.
Author(s): Kim, KangHyun, Kim, Sung-Min, Park, YoungMin, Lee, EunSol, Jung, SungJae, Kang, Jeongyong, An, DongUk, Min, Kyungil, Shim, Sung Ryul, Yu, Hyeong Won, Han, Hyun Wook
DOI: 10.1093/jamiaopen/ooae029
Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed.
Author(s): Baxter, Sally L, Longhurst, Christopher A, Millen, Marlene, Sitapati, Amy M, Tai-Seale, Ming
DOI: 10.1093/jamiaopen/ooae028
To evaluate patient-reported experiences of telehealth and disparities in access, use, and satisfaction with telehealth during the COVID-19 pandemic.
Author(s): Yoon, Esther, Hur, Scott, Curtis, Laura M, Benavente, Julia Yoshino, Wolf, Michael S, Serper, Marina
DOI: 10.1093/jamiaopen/ooae026