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To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).

Artificial intelligence (AI) is increasingly being used in healthcare settings, yet evidence of its real-world value remains inconsistent. Current evaluation paradigms often emphasize methodological rigor and technical validity over measurable improvements in patient outcomes or system performance.

Artificial intelligence (AI) is increasingly being deployed in health-care settings, yet health systems remain at an early stage in developing approaches to scale effective applications. Much of current activity is characterized by early-stage use cases and pilot projects. In this study, we explored how different health systems are approaching the development, adoption, implementation, and scaling of AI in healthcare to identify transferable lessons for system-level change.

To systematically examine different clinical data modalities in large language models (LLMs) and multimodal large language models (MLLMs), and to quantify the contribution of data modalities in early inpatient risk prediction and decision support tasks.

The COVID-19 pandemic accelerated the adoption of digital health technologies, including telehealth, electronic medical records, and patient-facing applications. These tools maintained access to care during periods of restriction but exposed gaps in workforce readiness, patient trust, digital literacy, and equity. Despite widespread implementation, limited evidence exists on how patients and healthcare providers experienced this transition and how these experiences influenced digital health adoption.

Human Factors (HF) principles are essential for safe and effective clinical decision support (CDS), yet existing guidance is fragmented and rarely evaluated in real world settings. A novel, evidence-based, vendor-agnostic HF-informed guideline was developed to address this gap. This study evaluated its perceived usefulness, usability, and impact on CDS design and optimization.

Evaluation of ambient AI on patient experience, documentation efficiency, clinician workload, and clinical throughput across a large emergency department (ED) network.