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There is much interest in utilizing clinical data for developing prediction models for Alzheimer's disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured electronic health record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.

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.

As computational methods for detecting symptoms can help us better attend to patient suffering, the objectives of this study were to develop and evaluate the performance of a natural language processing keyword library for detecting symptom talk, and to describe symptom communication within our dataset to generate insights for future model building.

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.

The aim of this study was to test the feasibility of PICO (participants, interventions, comparators, outcomes) entity extraction using weak supervision and natural language processing.

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.

Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews to better understand how AI could work in this context.

Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use.

Shared decision-making (SDM) is an approach in which patients and clinicians act as partners in making medical decisions. Patients receive the information needed to decide and are encouraged to balance risks, benefits, and preferences. Informative materials are vital to SDM. Atrial fibrillation (AF) is the most common cardiac arrhythmia and responsible for 10% of ischemic strokes, however 1/3 of patients are not on appropriate anticoagulation. Decision sharing may facilitate treatment [...]