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Electronic health record (EHR) data are prone to missingness and errors. Previously, we devised an enriched chart review protocol where a "roadmap" of auxiliary diagnoses was used to recover missing values. Still, chart reviews are expensive and time-intensive, limiting the number of patients whose data can be reviewed. Now, we investigate the accuracy and scalability of a roadmap-driven algorithm, based on International Classification of Diseases, 10th revision (ICD-10) codes, to [...]

Patient adherence to medication may be influenced by preferences regarding drug-product attributes. Product quality complaints (PQC) and medical information (MI) requests are a potential source of evidence on patients' preferences. The objective of this study was to demonstrate an approach for extracting and categorizing PQC and MI data using natural language processing (NLP) and quantify the relative desirability and acceptability of attributes of oral solid dosage formulations using sentiment analysis.

To develop a freely available, researcher-oriented system for accurate normalization of free-text medication strings to standardized RxNorm ingredient-level concepts.

Inflammatory bowel disease (IBD) consists of Crohn's disease (CD) and ulcerative colitis (UC) and is a spectrum autoimmune disease of the gastrointestinal tract. Large scale real-world evidence studies could provide valuable evidence about IBD for personalized healthcare recommendations. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standardizes electronic health record (EHR) data, allowing for research that incorporates multiple data sources. We are interested in whether OMOP CDM data [...]

This study investigates the feasibility of using large language models (LLMs) to automate procedural case log documentation in radiology training. We evaluate whether AI can replace manual logging, identify which procedure types are most challenging for extraction, and assess integration into clinical workflows.

Although machine learning (ML) holds significant potential to transform healthcare, there has been a recent surge in research output that often lacks methodological rigor, contributing to a reproducibility crisis. Additionally, the growing reliance on electronic health records (EHR) for developing ML models has heightened concerns about patient data privacy. To tackle these challenges, we have extended the open-source i2b2 (Informatics for Integrating Biology and the Bedside) platform to allow researchers [...]

The objective of this research was to examine the content and context-specific information diffusion patterns underlying communication pertaining to tobacco use from online health communities (OHCs).

Electronic health record (EHR) transitions disrupt health system workforces. Health professions trainees (HPTs) play a critical role in providing care yet are under-addressed during transitions. We apply learning health system (LHS) principles to improve HPTs' training experiences during the Veterans Affairs nationwide EHR transition.

The integration of artificial intelligence (AI) into health care is critically dependent on vast quantities of patient data, igniting an urgent global debate on data ownership, privacy, and governance. While numerous perspectives exist, the empirical structure and evolution of this scholarly discourse remain uncharacterized. We aimed to systematically map the conceptual landscape of research on AI and health data governance to identify its core themes, temporal trends, and key focus [...]

Early warning systems (EWSs) help clinicians identify deteriorating patients using clinical data, such as vital signs. However, standard systems struggle to capture nuanced nursing concerns. The Healthcare Process Model-ExpertSignals (HPM-ExpertSignals) framework describes how nurses' concerns are reflected in their documentation patterns. While a recent trial showed positive outcomes, the predictive gain of combining both data types remains unquantified.