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This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research.

In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates competency-driven AI instruction in medical school curriculum.

Studies that combine medical record and primary data are typically conducted in a small number of health care facilities (HCFs) covering a limited catchment area; however, depending on the study objectives, validity may be improved by recruiting a more expansive sample of patients receiving care across multiple HCFs. We evaluate the feasibility of a novel protocol to obtain patient medical records from multiple HCFs using a broad representative sampling frame.

To develop the architecture for a clinical decision support system (CDSS) linked to the electronic health record (EHR) using the tools provided by Research Electronic Data Capture (REDCap) to assess medication appropriateness in older adults with polypharmacy.

Electronic health records and many legacy systems contain rich longitudinal data that can be used for research; however, they typically are not readily available.

Biases within probabilistic electronic phenotyping algorithms are largely unexplored. In this work, we characterize differences in subgroup performance of phenotyping algorithms for Alzheimer's disease and related dementias (ADRD) in older adults.

When correcting for the "class imbalance" problem in medical data, the effects of resampling applied on classifier algorithms remain unclear. We examined the effect on performance over several combinations of classifiers and resampling ratios.

Electronic health records (EHRs) within the United States increasingly include sexual orientation and gender identity (SOGI) fields. We assess how well SOGI fields, along with International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes and medication records, identify gender-expansive patients.

To describe a user-centered approach to develop, pilot test, and refine requirements for 3 electronic health record (EHR)-integrated interventions that target key diagnostic process failures in hospitalized patients.

This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability.