Search AMIA.org

Although the main task of health care providers is to provide patient care, studies show that increasing amounts of time are spent on documentation.

School-aged children with chronic conditions require care coordination for health needs at school. Access to the student's accurate, real-time medical information is essential for school nurses to maximize their care of students. We aim to analyze school nurse access to medical records in a hospital-based electronic health record (EHR) and the effect on patient outcomes. We hypothesized that EHR access would decrease emergency department (ED) visits and inpatient hospitalizations.

To utilize metrics from physician action logs to analyze volume, physician efficiency and burden as impacted by telemedicine implementation during the COVID-19 (coronavirus disease 2019) pandemic, and physician characteristics such as gender, years since graduation, and specialty category.

This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses.

Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research.

People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such tasks) rely on self-reports, which are subject to recall and other biases. Few studies use wearable cameras and deep learning to capture and classify patient work activities automatically.

Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model's potential to introduce bias.