Search AMIA.org

To describe adaptations necessary for effective use of direct-to-consumer (DTC) cameras in an inpatient setting, from the perspective of health care workers.

Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to improve the overall patient population survival. Models developed by deep learning (DL) algorithms show good performance on many models. However, few DL algorithms have been validated in the dimension of survival time or compared [...]

Cardiac surgery patients are at high risk for readmissions after hospital discharge- few of these readmissions are preventable by mitigating barriers underlying discharge care transitions. An in-depth evaluation of the nuances underpinning the discharge process and the use of tools to support the process, along with insights on patient and clinician experiences, can inform the design of evidence-based strategies to reduce preventable readmissions.

The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis.

To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms.

We sought to ascertain perceived factors affecting women's career development efforts in the American Medical Informatics Association (AMIA) and to provide recommendations for improvements.

Electronic consultation (eConsult) content reflects important information about referring clinician needs across an organization, but is challenging to extract. The objective of this work was to develop machine learning models for classifying eConsult questions for question type and question content. Another objective of this work was to investigate the ability to solve this task with constrained expert time resources.

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 [...]