Electronic Health Records

Electronic health record research studies how to organize and learn from longitudinal patient data, including diagnoses, medications, laboratory results, clinical notes, and images. Common approaches include temporal sequence and foundation models for risk prediction, natural-language processing for extracting information from notes, and standards such as FHIR for interoperability; researchers also use knowledge graphs, synthetic data, and federated learning to address data access and variation across institutions. Applications range from forecasting outcomes and identifying disease patterns to clinical question answering, documentation support, and decision assistance. A central challenge is making systems reliable across incomplete, irregular, and institution-specific records while preventing data leakage and demonstrating safety and usefulness in real clinical workflows.