Correction to: Leveraging deep learning to detect stance in Spanish tweets on COVID-19 vaccination.
[This corrects the article DOI: 10.1093/jamiaopen/ooaf007.].
Author(s):
DOI: 10.1093/jamiaopen/ooaf028
[This corrects the article DOI: 10.1093/jamiaopen/ooaf007.].
Author(s):
DOI: 10.1093/jamiaopen/ooaf028
The Safety Assurance Factors for Electronic Health Record (EHR) Resilience (SAFER) Guides provide recommendations to healthcare organizations for conducting proactive self-assessments of the safety and effectiveness of their EHR implementation and use. Originally released in 2014, they were last updated in 2016. In 2022, the Centers for Medicare and Medicaid Services required their annual attestation by US hospitals.
Author(s): Sittig, Dean F, Flanagan, Trisha, Sengstack, Patricia, Cholankeril, Rosann T, Ehsan, Sara, Heidemann, Amanda, Murphy, Daniel R, Salmasian, Hojjat, Adelman, Jason S, Singh, Hardeep
DOI: 10.1093/jamia/ocaf018
Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data.
Author(s): Rong, Ruichen, Gu, Zifan, Lai, Hongyin, Nelson, Tanna L, Keller, Tony, Walker, Clark, Jin, Kevin W, Chen, Catherine, Navar, Ann Marie, Velasco, Ferdinand, Peterson, Eric D, Xiao, Guanghua, Yang, Donghan M, Xie, Yang
DOI: 10.1093/jamiaopen/ooaf026
The use of large language models (LLMs) is growing for both clinicians and patients. While researchers and clinicians have explored LLMs to manage patient portal messages and reduce burnout, there is less documentation about how patients use these tools to understand clinical notes and inform decision-making. This proof-of-concept study examined the reliability and accuracy of LLMs in responding to patient queries based on an open visit note.
Author(s): Salmi, Liz, Lewis, Dana M, Clarke, Jennifer L, Dong, Zhiyong, Fischmann, Rudy, McIntosh, Emily I, Sarabu, Chethan R, DesRoches, Catherine M
DOI: 10.1093/jamiaopen/ooaf021
To assess the capacity of a bespoke artificial intelligence (AI) process to help medical writers efficiently generate quality plain language summary abstracts (PLSAs).
Author(s): McMinn, David, Grant, Tom, DeFord-Watts, Laura, Porkess, Veronica, Lens, Margarita, Rapier, Christopher, Joe, Wilson Q, Becker, Timothy A, Bender, Walter
DOI: 10.1093/jamiaopen/ooaf023
Machine learning (ML) algorithms are promising tools for managing anemia in hemodialysis (HD) patients. However, their efficacy in predicting erythropoiesis-stimulating agents (ESAs) doses remains uncertain. This study aimed to evaluate the effectiveness of a contemporary artificial intelligence (AI) model in prescribing ESA doses compared to physicians for HD patients.
Author(s): Lim, Lee-Moay, Lin, Ming-Yen, Hsu, Chan, Ku, Chantung, Chen, Yi-Pei, Kang, Yihuang, Chiu, Yi-Wen
DOI: 10.1093/jamiaopen/ooaf020
Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.
Author(s): McInnis, Brian J, Pindus, Ramona, Kareem, Daniah H, Cakici, Julie, Vital, Daniela G, Hekler, Eric, Nebeker, Camille
DOI: 10.1093/jamia/ocaf004
To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.
Author(s): Kandaswamy, Swaminathan, Yarahuan, Julia K W, Dobler, Elizabeth A, Molloy, Matthew J, Knake, Lindsey A, Hernandez, Sean M, Fallon, Anne A, Hess, Lauren M, McCoy, Allison B, Fortunov, Regine M, Kirkendall, Eric S, Muthu, Naveen, Orenstein, Evan W, Dziorny, Adam C, Chaparro, Juan D
DOI: 10.1093/jamia/ocaf013
With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid [...]
Author(s): Miao, Chunlong, Luo, Jingjing, Liang, Yan, Liang, Hong, Cen, Yuhui, Guo, Shijie, Yu, Hongliu
DOI: 10.1093/jamia/ocae327
To describe the prevalence of and trends in practices that interfere with the exchange of patient health information (potential information blocking) 2 years after implementation of information-blocking regulations.
Author(s): Everson, Jordan, Healy, Daniel
DOI: 10.1093/jamia/ocaf007