JAMIA at 30: looking back and forward.
Author(s): Stead, William W, Miller, Randolph A, Ohno-Machado, Lucila, Bakken, Suzanne
DOI: 10.1093/jamia/ocad215
Author(s): Stead, William W, Miller, Randolph A, Ohno-Machado, Lucila, Bakken, Suzanne
DOI: 10.1093/jamia/ocad215
To assess the feasibility and implementation, usability, acceptability and efficacy of virtual reality (VR), and augmented reality (AR) smartphone applications for upskilling care home workers in hand hygiene and to explore underlying learning mechanisms.
Author(s): Gasteiger, Norina, van der Veer, Sabine N, Wilson, Paul, Dowding, Dawn
DOI: 10.1093/jamia/ocad200
Clinical decision support systems (CDSS) were implemented in community pharmacies over 40 years ago. However, unlike CDSS studies in other health settings, few studies have been undertaken to evaluate and improve their use in community pharmacies, where billions of prescriptions are filled every year. The aim of this scoping review is to summarize what research has been done surrounding CDSS in community pharmacies and call for rigorous research in this area.
Author(s): Moon, Jukrin, Chladek, Jason S, Wilson, Paije, Chui, Michelle A
DOI: 10.1093/jamia/ocad208
The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule."
Author(s): Davis, Sharon E, Matheny, Michael E, Balu, Suresh, Sendak, Mark P
DOI: 10.1093/jamia/ocad178
The classification of clinical note sections is a critical step before doing more fine-grained natural language processing tasks such as social determinants of health extraction and temporal information extraction. Often, clinical note section classification models that achieve high accuracy for 1 institution experience a large drop of accuracy when transferred to another institution. The objective of this study is to develop methods that classify clinical note sections under the SOAP [...]
Author(s): Zhou, Weipeng, Yetisgen, Meliha, Afshar, Majid, Gao, Yanjun, Savova, Guergana, Miller, Timothy A
DOI: 10.1093/jamia/ocad190
We examined the influence of 4 different risk information formats on inpatient nurses' preferences and decisions with an acute clinical deterioration decision-support system.
Author(s): Jeffery, Alvin D, Reale, Carrie, Faiman, Janelle, Borkowski, Vera, Beebe, Russ, Matheny, Michael E, Anders, Shilo
DOI: 10.1093/jamia/ocad209
Automatic detection of atrial fibrillation and flutter (AF/AFL) is a significant concern in preventing stroke and mitigating hemodynamic instability. Herein, we developed a Transformer-based deep learning model for AF/AFL segmentation in single-lead electrocardiograms (ECGs) by self-supervised learning with masked signal modeling (MSM).
Author(s): Yun, Donghwan, Yang, Hyun-Lim, Kwon, Soonil, Lee, So-Ryoung, Kim, Kyungju, Kim, Kwangsoo, Lee, Hyung-Chul, Jung, Chul-Woo, Kim, Yon Su, Han, Seung Seok
DOI: 10.1093/jamia/ocad219
Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ).
Author(s): Madandola, Olatunde O, Bjarnadottir, Ragnhildur I, Yao, Yingwei, Ansell, Margaret, Dos Santos, Fabiana, Cho, Hwayoung, Dunn Lopez, Karen, Macieira, Tamara G R, Keenan, Gail M
DOI: 10.1093/jamia/ocad188
Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative knowledge graph to construct clusters of rare diseases.
Author(s): Sanjak, Jaleal, Binder, Jessica, Yadaw, Arjun Singh, Zhu, Qian, Mathé, Ewy A
DOI: 10.1093/jamia/ocad186
Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence.
Author(s): Li, Piaopiao, Lyu, Tianchen, Alkhuzam, Khalid, Spector, Eliot, Donahoo, William T, Bost, Sarah, Wu, Yonghui, Hogan, William R, Prosperi, Mattia, Schatz, Desmond A, Atkinson, Mark A, Haller, Michael J, Shenkman, Elizabeth A, Guo, Yi, Bian, Jiang, Shao, Hui
DOI: 10.1093/jamia/ocad194