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To characterize and demonstrate how to reduce the administrative burden experienced by patients when navigating medication affordability resources in the United States.

To synthesize knowledge on tensions characterizing large-scale electronic health record (EHR) implementations.

Machine learning algorithms can advance clinical care, including identifying mental health conditions. These algorithms are often developed without considering the perspectives of the affected populations. This study describes the process of incorporating end-user perspectives into the development and implementation planning of a prediction algorithm for new perinatal depression onset.

Screening Brief Intervention and Referral to Treatment (SBIRT) can reduce the health and social costs associated with unhealthy alcohol use (UAU). Electronic health records (EHRs) can support evidence-based screening practices for UAU and provide performance data needed for quality improvement. The objective of this study was to describe barriers faced by primary care clinics when using EHR systems to support UAU screening and delivery of recommended interventions.

To conduct a scoping review (ScR) of existing approaches for synthetic Electronic Health Records (EHR) data generation, to benchmark major methods, and to provide an open-source software and offer recommendations for practitioners.

Establishing a robust and secure framework allowing creation and sharing of custom features within the REDCap electronic data capture platform.

Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source large language models (LLMs) guided by prompt instructions and chain of thought (CoT) for this task.

The US healthcare system faces significant challenges, including clinician burnout, operational inefficiencies, and concerns about patient safety. Artificial intelligence (AI), particularly generative AI, has the potential to address these challenges, but its adoption, effectiveness, and barriers to implementation are not well understood.

To examine the discrimination, calibration, and algorithmic fairness of the Epic End of Life Care Index (EOL-CI).