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Medical product safety surveillance efforts, whether using electronic health record (EHR) or claims data, typically rely on structured codes. Utilizing unstructured EHR data, particularly information extracted from clinical text through natural language processing (NLP), enriches information available for data mining, phenotyping, and surveillance. To assess overlapping and distinct information across structured and unstructured EHR data, we mapped both to a common vocabulary (Medical Dictionary for Regulatory Activities, MedDRA). We assess [...]

This study evaluates the completeness of diagnostic information captured in electronic health records (EHRs) compared to administrative claims data across multiple U.S. healthcare organizations between 2010 and 2022.

To evaluate CigStopper, a machine learning algorithm designed to predict billing eligibility for tobacco cessation counseling (CPT 99406/99407), addressing persistent underbilling and documentation gaps in health systems.

This study examined the use of machine learning (ML) and domain-specific enrichment in patient-generated health data, in the form of free-text meal logs, to classify meals on alignment with different nutritional goals.

Rheumatoid and Psoriatic Arthritis (RA and PsA) are autoimmune diseases that cause debilitating joint pain. Disease-modifying antirheumatic drugs (DMARDs) are recommended for the treatment of both conditions. However, real-world evidence studies would help characterize compliance with these 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 determining whether OMOP CDM [...]

We report on using electronic health records (EHRs) and other health information technology (IT) (eg, REDCap, Excel, and population-health tools) for tracking patients and managing interventions to improve colorectal screening (CRC) among primary care practices who participated in the National Cancer Institute's Accelerating Colorectal Cancer Screening and Follow-up through Implementation Science (ACCSIS) program.

Enabling the unambiguous communication of cancer genetic testing results and the corresponding therapeutic implications of identified genetic variants is of major clinical importance. In this report, we assess the adequacy of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) Genomics Reporting Implementation Guide, version 3 (also known as "FHIR Genomics") for the structured communication of semantic objects typically appearing in real-world somatic testing reports.

To describe the development and early implementation of the Digital Health Equity & Literacy Program (D-HELP), a student-led quality improvement initiative to promote digital health engagement in the emergency department (ED).

Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into artificial intelligence (AI) remains challenging. Previous approaches, such as rule-based systems or black-box AI models, face significant limitations, including poor interpretability, inconsistent adherence to guidelines, and narrow domain applicability. To address this, we develop and validate CPGPrompt, an auto-prompting system that converts narrative clinical guidelines into large language models (LLMs).

Temporal drift, defined as changes over time in underlying data distributions, can degrade the performance of clinical prediction models. In head and neck cancer (HNC) radiotherapy, evolving proton and carbon ion therapies may shift the risk of oral mucositis over time. This study aimed to compare machine learning (ML) strategies for mitigating temporal drift in predicting grade ≥2 oral mucositis among patients treated with particle therapy.