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Description

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.

Learning Objective 1: Reconcile inconsistencies between clinical diagnoses and administrative records by analyzing free-text notes, problem lists and recorded diagnoses in real time

Authors:

Yufan Guo, IBM
Joy Wu, IBM
Tyler Baldwin (Presenter)
IBM

David Beymer, IBM
Vandana Mukherjee, IBM
Tanveer Syeda-Mahmood, IBM

Presentation Materials:

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