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Machine learning (ML)-based cluster analysis is a common method for subtyping medical conditions and presentations of disease states. Recent advancements in algorithms and increasing access to vast healthcare data have further increased the use of such ML models. However, practical implementation is lacking, largely due to methodological limitations and insufficient reporting and clinical contextualization in the extant literature, with no existing guidelines for this purpose.

We present a method and open-source prototype to augment Clinical Quality Language (CQL) using a large language model (LLM) in the execution loop for querying unstructured text.

This study evaluated the optimization of a medication-related clinical decision support system (MRCDSS) in a 1000-bed general hospital in China by analyzing clinician responses.

To evaluate the utility of large language models (LLMs) without and with retrieval-augmented generation (RAG) resources for automating Research Electronic Data Capture (REDCap) support at a large academic medical center, focusing on ticket complexity, categorization, and response drafting.

This study examines disparities in the use of interoperability features within the EHR among family medicine physicians in the United States, particularly in areas with higher social deprivation index and rural settings.

Artificial intelligence is used in healthcare to identify and manage health conditions across diverse patient populations and clinical settings, but biases in algorithms can perpetuate and exacerbate inequalities in health and healthcare delivery. While some research has focused on addressing this critical issue, there is a lack of comprehensive information on the types of strategies employed for bias mitigation in the use of artificial intelligence in healthcare and the effectiveness [...]

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 [...]