To address database interoperability challenges to improve collaboration among disparate organizations.
Author(s): DeFranco, Joanna F, Roberts, Joshua, Ferraiolo, David, Compton, D Chris
DOI: 10.1093/jamiaopen/ooae040
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
The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.
Author(s): Essaid, Shahim, Andre, Jeff, Brooks, Ian M, Hohman, Katherine H, Hull, Madelyne, Jackson, Sandra L, Kahn, Michael G, Kraus, Emily M, Mandadi, Neha, Martinez, Amanda K, Mui, Joyce Y, Zambarano, Bob, Soares, Andrey
DOI: 10.1093/jamiaopen/ooae045
Machine learning (ML) will have a large impact on medicine and accessibility is important. This study's model was used to explore various concepts including how varying features of a model impacted behavior.
Author(s): Lee, Stephen B
DOI: 10.1093/jamiaopen/ooae035
The objective was to identify information loss that could affect clinical care in laboratory data transmission between 2 health care institutions via a Health Information Exchange platform.
Author(s): Luu, Hung S, Campbell, Walter S, Cholan, Raja A, Edgerton, Mary E, Englund, Andrea, Keller, Alana, Korte, Elizabeth D, Mitchell, Sandra H, Watkins, Greg T, Westervelt, Lindsay, Wyman, Daniel, Powell, Stephen
DOI: 10.1093/jamiaopen/ooae032
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
We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers.
Author(s): Barata, Filipe, Shim, Jinjoo, Wu, Fan, Langer, Patrick, Fleisch, Elgar
DOI: 10.1093/jamiaopen/ooae027
To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models.
Author(s): Scott, Ian A, De Guzman, Keshia R, Falconer, Nazanin, Canaris, Stephen, Bonilla, Oscar, McPhail, Steven M, Marxen, Sven, Van Garderen, Aaron, Abdel-Hafez, Ahmad, Barras, Michael
DOI: 10.1093/jamiaopen/ooae031
[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].
Author(s):
DOI: 10.1093/jamiaopen/ooae046