Correction to: Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures.
Author(s):
DOI: 10.1093/jamia/ocac224
Author(s):
DOI: 10.1093/jamia/ocac224
Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature.
Author(s): Hatherley, Joshua, Sparrow, Robert
DOI: 10.1093/jamia/ocac218
To summarize the research literature evaluating automated methods for early detection of safety problems with health information technology (HIT).
Author(s): Surian, Didi, Wang, Ying, Coiera, Enrico, Magrabi, Farah
DOI: 10.1093/jamia/ocac220
To determine if the Conexion digital localized health information resource about diabetes and depression could increase patient activation among Hispanic low-income adults.
Author(s): Zhang, Tianmai M, Millery, Mari, Aguirre, Alejandra N, Kukafka, Rita
DOI: 10.1093/jamia/ocac213
To access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems.
Author(s): McDonald, Clement J, Baik, Seo H, Zheng, Zhaonian, Amos, Liz, Luan, Xiaocheng, Marsolo, Keith, Qualls, Laura
DOI: 10.1093/jamia/ocac215
This study aims to develop a convolutional neural network-based learning framework called domain knowledge-infused convolutional neural network (DK-CNN) for retrieving clinically similar patient and to personalize the prediction of macrovascular complication using the retrieved patients.
Author(s): Oei, Ronald Wihal, Hsu, Wynne, Lee, Mong Li, Tan, Ngiap Chuan
DOI: 10.1093/jamia/ocac212
We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout.
Author(s): Apathy, Nate C, Hare, Allison J, Fendrich, Sarah, Cross, Dori A
DOI: 10.1093/jamia/ocac211
To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift.
Author(s): Smith, Ari J, Patterson, Brian W, Pulia, Michael S, Mayer, John, Schwei, Rebecca J, Nagarajan, Radha, Liao, Frank, Shah, Manish N, Boutilier, Justin J
DOI: 10.1093/jamia/ocac214
Author(s):
DOI: 10.1093/jamia/ocac206
To identify and characterize clinical subgroups of hospitalized Coronavirus Disease 2019 (COVID-19) patients.
Author(s): Ta, Casey N, Zucker, Jason E, Chiu, Po-Hsiang, Fang, Yilu, Natarajan, Karthik, Weng, Chunhua
DOI: 10.1093/jamia/ocac208