Search AMIA.org

To compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician-computer models.

To assess primary care teams' perceptions of a health information exchange (HIE) event notification intervention for geriatric patients in 2 Veterans Health Administration (VHA) medical centers.

We quantify the use of clinical decision support (CDS) and the specific barriers reported by ambulatory clinics and examine whether CDS utilization and barriers differed based on clinics' affiliation with health systems, providing a benchmark for future empirical research and policies related to this topic.

To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.

This study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.

The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production [...]

To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults.

The study sought to assist practitioners in identifying and prioritizing radiography exams that are more likely to contain abnormalities, and provide them with a diagnosis in order to manage heavy workload more efficiently (eg, during a pandemic) or avoid mistakes due to tiredness.

To measure nurse-perceived electronic health records (EHR) usability with a standardized metric of technology usability and evaluate its association with professional burnout.

We propose a bidirectional GPS imputation method that can recover real-world mobility trajectories even when a substantial proportion of the data are missing. The time complexity of our online method is linear in the sample size, and it provides accurate estimates on daily or hourly summary statistics such as time spent at home and distance traveled.