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Problem-oriented documentation is an accepted method of note construction which facilitates clinical thought processes. However, problem-oriented documentation is challenging to put into practice using commercially available electronic health record (EHR) systems.

Research recruitment through patient portals (ie, patient-facing, web-based clinical interfaces) has the potential to be effective, efficient, and inclusive, but best practices remain undefined. We sought to better understand how patients view this recruitment approach.

Automatically identifying patients at risk of immune checkpoint inhibitor (ICI)-induced colitis allows physicians to improve patientcare. However, predictive models require training data curated from electronic health records (EHR). Our objective is to automatically identify notes documenting ICI-colitis cases to accelerate data curation.

Enabling discovery across the spectrum of rare and common diseases requires the integration of biological knowledge with clinical data; however, differences in terminologies present a major barrier. For example, the Human Phenotype Ontology (HPO) is the primary vocabulary for describing features of rare diseases, while most clinical encounters use International Classification of Diseases (ICD) billing codes. ICD codes are further organized into clinically meaningful phenotypes via phecodes. Despite their prevalence [...]

This study aimed to understand how a metaverse-based (virtual) workspace can be used to support the communication and collaboration in an academic health informatics lab.

Coronavirus disease (COVID)-related misinformation is prevalent online, including on social media. The purpose of this study was to explore factors associated with user engagement with COVID-related misinformation on the social media platform, TikTok. A sample of TikTok videos associated with the hashtag #coronavirus was downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, and high) using a codebook developed by experts in infectious diseases. Multivariable modeling [...]

Inter- and intra-observer variability is a concern for medical school admissions. Artificial intelligence (AI) may present an opportunity to apply a fair standard to all applicants systematically and yet maintain sensitivity to nuances that have been a part of traditional screening methods.

To provide a method for evaluating the interoperability of Fast Healthcare Interoperability Resources (FHIR®) clients and servers supporting different FHIR resources, profiles, and versions, and to determine the feasibility of FHIR servers supporting multiple FHIR Implementation Guides (IGs).

A shallow convolutional neural network (CNN), TextCNN, has become nearly ubiquitous for classification among clinical and medical text. This research presents a novel eXplainable-AI (X-AI) software, Red Flag/Blue Flag (RFBF), designed for binary classification with TextCNN. RFBF visualizes each convolutional filter's discriminative capability. This is a more informative approach than direct assessment of logit contribution, features that overfit to train set nuances on smaller datasets may indiscriminately activate large logits [...]

The objective of this study was to systematically review all literature studying the effect of patient education on patient engagement through patient portals.