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We evaluated the data requirement for modern AI tools to outperform simpler models in predicting short-term mortality in over 500 000 patients with hemodialysis-dependent kidney failure.

To assess patient awareness, trust, perceived benefits, and risks of artificial intelligence (AI) in clinical care within an urban safety-net health system.

To address the gap between artificial intelligence (AI) model development and clinical implementation by developing a machine-learning model to predict cochlear implant (CI) eligibility based on routine hearing tests, while illustrating 3 practical lessons: reformulation for clinical utility, selection of problem-specific metrics, and handling input data variability.

To develop and validate a prespecified logistic model for detecting mild cognitive impairment (MCI) using MyCog Mobile, a self-administered smartphone-based screening application, and to evaluate a structured simplification that reduces patient burden while maintaining diagnostic accuracy.

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.

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.