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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.

Tumor registries are a rich source of real-world data which can be used to test important hypotheses that inform clinical care. Exploratory data analysis at the level of individual subjects, when enhanced by interactive data visualizations, has the potential to provide novel insights and generate new hypothesis.

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.

To assess the extent to which health disparities content is integrated in multidisciplinary health informatics training programs and examine instructor perspectives surrounding teaching strategies and challenges, including student engagement with course material.

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.

Post-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC.

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.

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.

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.