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Data governance, the policies, and procedures for managing data, is a critical factor for secondary use of clinical data for research.

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.

Does a Tree-of-Thought prompt and reconsideration of Isabel Pro's differential improve ChatGPT-4's accuracy; does increasing expert panel size improve ChatGPT-4's accuracy; does ChatGPT-4 produce consistent outputs in sequential requests; what is the frequency of fabricated references?

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.

We analyzed interoperability-related Real World Testing results to identify whether developers are providing meaningful results with the appropriate context to enable stakeholders to understand the Certified Health IT conformance and interoperability when deployed in production environments.

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.

Telemedicine for specialty medical care is evolving from a COVID-19 pandemic-era requirement to an option for patients and clinicians alike, requiring evidence to guide optimal use of virtual specialty care. Heterogeneity across medical specialties complicates this evidence generation. To address this gap in the literature, we present an approach to evaluation of telehealth across specialties with the potential to generate findings generalizable across specialties and health systems.

This study aims to explore multimorbidity patterns associated with diabetes by analyzing user engagement in online diabetes support communities and their interactions with other disease-related communities. Additionally, it seeks to assess whether early signals of diabetes can be detected through online engagement data.