To report on the feasibility of a simultaneous, enterprise-wide deployment of EHR-integrated ambient scribe technology across a large academic health system.
Author(s): Wright, Aileen P, Nall, Carolynn K, Franklin, Jacob J H, Horst, Sara N, Kumah-Crystal, Yaa A, Wright, Adam T, Mize, Dara E
DOI: 10.1093/jamia/ocaf186
This study aims to evaluate physicians' practices and perspectives regarding large language models (LLMs) in healthcare settings.
Author(s): Hong, Hyo Jung, Shah, Nigam, Pfeffer, Michael Adam, Lehmann, Lisa S
DOI: 10.1055/a-2735-0527
Estimating readmission risk for intensive care unit (ICU) patients is critical for clinicians to optimize resource allocation and prevent premature discharges. Machine learning models currently applied to this task either lack interpretability or cannot identify patient subgroups with distinctive readmission risks and characteristics. We addressed this gap by introducing a cutting-edge rule-based model, namely truly unordered rule sets (TURS), to reveal heterogeneous readmission risks and subgroup-level patient characteristics.
Author(s): Yang, Lincen, van der Meijden, Siri L, Arbous, Sesmu M, van Leeuwen, Matthijs
DOI: 10.1093/jamia/ocaf171
To inform initiatives to improve the interoperability of healthcare data, we described the experience of distinct phenotypes of physicians when obtaining information from outside sources.
Author(s): Everson, Jordan, Strawley, Catherine
DOI: 10.1093/jamia/ocaf178