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Cardiac surgeries are associated with a range of serious postoperative complications, and current predictive models often rely on static summaries of preoperative and intraoperative data compiled after the case. Such development limits their utility in real-time clinical settings. To address this challenge, we developed TE-Transformer, an end-to-end deep learning framework that continuously ingests heterogeneous electronic health record (EHR) data-including preoperative variables, high-frequency vital signs, medication administration records, and brief surgical [...]

To demonstrate a replicable in-house framework for evaluating ambient digital scribe (ADS) systems and, applying it, to assess 3 commercial systems and examine alignment between clinician Likert ratings and a manual transcript-level error audit.

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.

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.

This study aimed to identify trajectories of multimorbidity following acute myocardial infarction (AMI), using explainable temporal machine-learning methods, and assess their clinical, prognostic, and biological significance.

The potential of "big data" in health research remains largely untapped, particularly concerning real-world data sources such as administrative health data and electronic medical records. While healthcare insurance claims data have been essential for assessing medication safety and effectiveness within indicated patient populations, exploring broader drug-outcome associations could uncover significant insights.

To develop and systematically compare a human-led and LLM-assisted hybrid deductive-inductive workflow for qualitative analyses.