Search AMIA.org

This case study assesses the uptake, user characteristics, and outcomes of automated self-scheduling in a community-based physician group affiliated with an academic health system. We analyzed 1 995 909 appointments booked between January 1, 2019, and June 30, 2021 at more than 30 practice sites. Over the study period, uptake of self-scheduling increased from 4% to 15% of kept appointments. Younger, commercially insured patients were more likely to be users. Missed appointments [...]

Deep learning models for clinical event forecasting (CEF) based on a patient's medical history have improved significantly over the past decade. However, their transition into practice has been limited, particularly for diseases with very low prevalence. In this paper, we introduce CEF-CL, a novel method based on contrastive learning to forecast in the face of a limited number of positive training instances.

The purpose of the study was to develop and validate a model to predict the risk of experiencing a fall for nursing home residents utilizing data that are electronically available at the more than 15 000 facilities in the United States.

After a new electronic health record (EHR) was implemented at Mayo Clinic, a training program called reBoot Camp was created to enhance ongoing education in response to needs identified by physician leaders.

Both academic medical centers and biomedical research sponsors need to understand impact of scientific funding to determine value. For the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) hubs, tracking research activities can be complex, often involving multiple institutions and continually changing federal reporting requirements. Existing research administrative systems are institution-specific and tend to focus only on parts of a greater whole. The goal of this case [...]

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.

We aim to investigate the application and accuracy of artificial intelligence (AI) methods for automated medical literature screening for systematic reviews.