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The digitalization of health records stands to improve decision-making at clinical, administrative, and policy level. Efforts follow various paths and are closely intertwined with health system and organizational configurations. Problems persist in both uptake and use. This study explores the digitalization trajectories of academic health centers (AHCs) to understand tensions between organizational and government strategies and their impact on digital development.

Health equity is greatly impacted by the systems and processes with which health systems deliver care. Given the minimal guidance on measurement and reporting of health inequities specific to key population health outcomes, a solution for measurement of health equity is proposed.

Health professional (HP) trainee burnout is hard to capture. There are many validated quantitative tools to assess trainee burnout, but fewer qualitative methodological tools that can elicit rich and trustworthy qualitative data on HP trainee burnout.

Quantify the effect of ambient artificial intelligence (AI) scribe technology on work experience, clinical operations, and patient experience in pediatric primary care.

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?

To describe challenges and solutions for calculating longitudinal daily opioid dose in morphine milligram equivalents from electronic health record prescriptions for a clinical trial of voluntary opioid reduction in patients with chronic non-cancer pain.

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.

To develop CigStopper, a real-time, automated medical billing prototype designed to identify eligible tobacco cessation care codes, thereby reducing administrative workload while improving billing accuracy.

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.