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Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data.

The timely initiation of renal replacement therapy (RRT) for acute kidney injury (AKI) requires sequential decision-making tailored to individuals' evolving characteristics. To learn and validate optimal strategies for RRT initiation, we used reinforcement learning on clinical data from routine care and randomized controlled trials.

To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule.

Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants.

The aim of this study was to disseminate insights from a nationwide pilot of the International Classification of Diseases-11th revision (ICD-11).

With an increasing focus on the digitalization of health and care settings, there is significant scope to learn from international approaches to promote concerted adoption of electronic health records.

The increasing demands for curated, high-quality research data are driving the emergence of a novel registry type. The need to assemble, curate, and export this data grows, and the conventional simplicity of registry models is driving the need for advanced, multimodal data registries-the dawn of the next-generation registry.

The study aims to assess racial and language disparities in pediatric emergency department (ED) triage using analytical techniques and provide insights into the extent and nature of the disparities in the ED setting.

Despite federally mandated collection of sex and gender demographics in the electronic health record (EHR), longitudinal assessments are lacking. We assessed sex and gender demographic field utilization using EHR metadata.