EMR systems are intended to improve patient-centered care management and hospital administrative processing. However, the information stored in EMRs can be disorganized, incomplete, or inconsistent, creating problems at the patient and system level. We present a technology that reconciles inconsistencies between clinical diagnoses and administrative records by analyzing free-text notes, problem lists and recorded diagnoses in real time. A fully integrated pipeline has been developed for efficient, knowledge-driven extraction, normalization, and matching of disease terms among structured and unstructured data, with modular precision of 94-98% on over 1000 patients. This cognitive data review tool improves the path from diagnosis to documentation, facilitating accurate and timely clinical and administrative decision-making.