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The purpose of the study was to determine if association exists between evidence-based provider training and clinician proficiency in electronic health record (EHR) use and if so, which EHR use metrics and vendor-defined indices exhibited association.

Machine learning is used to understand and track influenza-related content on social media. Because these systems are used at scale, they have the potential to adversely impact the people they are built to help. In this study, we explore the biases of different machine learning methods for the specific task of detecting influenza-related content. We compare the performance of each model on tweets written in Standard American English (SAE) vs [...]

The study sought to learn if it were possible to develop an ontology that would allow the Food and Drug Administration approved indications to be expressed in a manner computable and comparable to what is expressed in an electronic health record.

Open notes invite patients and families to read ambulatory visit notes through the patient portal. Little is known about the extent to which they identify and speak up about perceived errors. Understanding the barriers to speaking up can inform quality improvements.

This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data.

Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes 2 contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution [...]

To develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs).

This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic.

Health and biomedical informatics graduate-level degree programs have proliferated across the United States in the last 10 years. To help inform programs on practices in teaching and learning, a survey of master's programs in health and biomedical informatics in the United States was conducted to determine the national landscape of culminating experiences including capstone projects, research theses, internships, and practicums. Almost all respondents reported that their programs required a culminating [...]