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To assess whether a fall-prevention clinical decision support (CDS) approach using electronic analytics that stimulates risk-targeted interventions is associated with reduced rates of falls and injurious falls.

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

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.

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.

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.

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

The aim of this study was to design and assess the formative usability of a novel patient portal intervention designed to empower patients with diabetes to initiate orders for diabetes-related monitoring and preventive services.

Weak supervision holds significant promise to improve clinical natural language processing by leveraging domain resources and expertise instead of large manually annotated datasets alone. Here, our objective is to evaluate a weak supervision approach to extract spatial information from radiology reports.

Our objective is to assess the accuracy of the COVID-19 vaccination status within the electronic health record (EHR) for a panel of patients in a primary care practice when manual queries of the state immunization databases are required to access outside immunization records.

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.