Author(s): Alper, Brian S, Dehnbostel, Joanne, Lehmann, Harold
DOI: 10.1093/jamia/ocag024
Physician and advanced practice provider (APP) well-being is a critical focus in healthcare. Emerging technology such as generative artificial intelligence (GAI) scribes reduces physician and APP administrative burden created by electronic health records. Early adopters of this technology have demonstrated promising improvements in clinical documentation, well-being, and cognitive load. However, further exploration across professional roles is warranted.
Author(s): Schneider, Kathryn R, Swann-Thomsen, Hillary E, Ribbens, Terry G, Bahnmaier, Lucas A, Satterfield, Trevor, Pullicar, Reme, Soni, Neeraj
DOI: 10.1093/jamia/ocag005
Author(s): Zablah, Jose, Molina, Yolly, Garcia Loureiro, Antonio
DOI: 10.1093/jamia/ocag023
Author(s): Cheng, Weihao
DOI: 10.1093/jamia/ocag022
Our study aims to assess the time-cost burden reduction of transitioning from manual case reporting to electronic case reporting (eCR) for COVID-19 among healthcare organizations (HCOs) over a 1-year period.
Author(s): Rincón-Guevara, Oscar, Olorukooba, Abdulhakeem A, Eau, Grace, Ritchey, Matthew D, Conn, Laura A, Knicely, Kimberly
DOI: 10.1093/jamia/ocag011
Efficient exchange of health information requires consistent representation of clinical concepts across laboratories, hospitals, and public health systems. LOINC supports this interoperability by standardizing laboratory test codes, but mapping remains difficult when datasets are incomplete, inconsistently formatted, or structurally diverse. These challenges often create a mismatch between algorithmic performance in controlled settings and real-world deployment. This study aimed to develop a biomedical natural language processing (NLP) approach for mapping heterogeneous [...]
Author(s): Naliyatthaliyazchayil, Parvati, Sangam, Venkat Ramana, Amlung, Joseph, Kanter, Andrew S, Purkayastha, Saptarshi, Payne, Jonathan
DOI: 10.1093/jamia/ocag010
To comprehensively evaluate the validity of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for both prevalent diagnoses and less common diseases, and to assess the performance of a large language model (LLM)-based system in validating these codes.
Author(s): Wang, Yichen, Song, Yilin, Siu, Rex, Nimma, Induja R, Yan, Yan, Savage, Thomas R, Wang, Yiming, Li, Zhichen, Ramai, Daryl, Wang, Jiale, Badurdeen, Dilhana, Tao, Cui, Kumbhari, Vivek, Huang, Yuting
DOI: 10.1093/jamia/ocag008
The use of ambient AI documentation tools is rapidly growing in US hospitals and clinics. Such tools generate the first draft of clinical notes from scribed patient-provider conversations, which clinicians can then review and edit before signing into electronic health records (EHR). Understanding how and why clinicians make modifications to AI-generated drafts is critical to improving AI design and clinical efficiency, yet it has been under-studied. This study aims to [...]
Author(s): Guo, Yawen, Hu, Di, Yang, Ziqi, Chow, Emilie, Tam, Steven, Perret, Danielle, Pandita, Deepti, Zheng, Kai
DOI: 10.1093/jamia/ocag059
Biobanks are essential for intelligent medicine but face fragmentation and heterogeneity. No standardized framework exists for assessing biobank data value using public information; this study addresses this gap.
Author(s): Yang, Yin, Ullah, Amin, Zhang, Yingbo, Zong, Hui, Liu, Xingyun, Zhang, Chi, Hu, Shanshan, Li, Jiakun, Shen, Bairong
DOI: 10.1093/jamia/ocag052
Using 15 years of hospital survey data (2008-2023), we examined rural-urban trends in electronic health records (EHR) adoption and advanced capabilities for data sharing, public health reporting, and care improvements.
Author(s): Anzalone, A Jerrod, Liu, Yinting, Reisher, Elizabeth, Frankel, Emily, Hansen, Jed R, Geary, Carol Reynolds
DOI: 10.1093/jamia/ocag043