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Conversational Health Agents (CHAs) are interactive systems providing healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Language Models (LLMs), primarily focus on conversation aspects. However, they offer limited agent capabilities, specifically needing more multistep problem-solving, personalized conversations, and multimodal data analysis. We aim to overcome these limitations.

To assess the effect of a digital scribe among pediatric providers on documentation time, cognitive load, burnout, and caregiver satisfaction.

To develop and apply a reproducible methodology for evaluating generative artificial intelligence (AI) powered systems in health care, addressing the gap between theoretical evaluation frameworks and practical implementation guidance.

This study aimed to systematically evaluate and compare the diagnostic performance of leading large language models (LLMs) in common and complex clinical scenarios, assessing their potential for enhancing clinical reasoning and diagnostic accuracy in authentic clinical decision-making processes.

We used clinical decision support (CDS) to promote compliance with the 21st Century Cures Act's mandate that, with few exceptions, patients be granted timely access to their clinical notes.

In response to requests from several Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) community-engaged research projects to include Long COVID common data elements (CDEs) in the existing RADx-UP CDEs, the RADx-UP Coordination and Data Collection Center (CDCC) leadership formed the Long COVID CDEs Task Force.

Little is known about how clinical decision support (CDS) tools can support care teams in changing clinical decisions to account for patients' social risks. We piloted a suite of electronic health record (EHR)-based CDS tools designed to facilitate social risk-informed care decisions to assess how the tools were used in practice and how they could be improved.

Despite growing recognition of the critical role of nutrition in promoting population health, clinicians lack access to point-of-care resources to support culturally relevant nutrition services. This study aims to (1) evaluate Registered Dietitian Nutritionists' (RDN) likelihood of using a web-based tool to provide culturally- and medically tailored nutrition services, (2) identify needed or preferred features, and (3) examine concerns related to the development or implementation of a web-based tool.

The main goal is to develop an automated process for connecting Research Electronic Data Capture (REDCap) instances in a clinical trial network to allow for deidentified transfer of research surveys to cloud computing data commons for discovery.

This work aims to provide a specialized template for integrating diverse study data types with varying sharing restrictions specific to NIH HEAL Initiative chronic pain and substance use research.