Search AMIA.org

Patient access to health information using electronic patient portals is increasingly common. Portal use has the potential to improve patients' engagement with their health and is particularly important for patients with chronic illness; however, patients' abilities, attitudes, and use of portals are poorly understood.

The Objective Structured Assessment of Debriefing (OSAD) is an evidence-based, 8-item tool that uses a behaviorally anchored rating scale in paper-based form to evaluate the quality of debriefing in medical education. The objective of this project was twofold: 1) to create an easy-to-use electronic format of the OSAD (eOSAD) in order to streamline data entry; and 2) to pilot its use on videoed debriefings.

To examine roles for summer internship programs in expanding pathways into biomedical informatics, based on 10 years of the Vanderbilt Department of Biomedical Informatics (DBMI) Summer Research Internship Program.

The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.

Hospitalized patients often receive opioids. There is a lack of consensus regarding evidence-based guidelines or training programs for effective management of pain in the hospital. We investigated the viability of using an Internet-based opioid dosing simulator to teach residents appropriate use of opioids to treat and manage acute pain.

To compare physician productivity and billing before and after implementation of electronic charting in an academic emergency department (ED).

To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload.

To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open challenges in building deep learning models of EHRs.

Location data are becoming easier to obtain and are now bundled with other metadata in a variety of biomedical research applications. At the same time, the level of sophistication required to protect patient privacy is also increasing. In this article, we provide guidance for institutional review boards (IRBs) to make informed decisions about privacy protections in protocols involving location data. We provide an overview of some of the major categories [...]

The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.