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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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An electronic health record based on structured narrative.

Stephen B Johnson¹, Suzanne Bakken, Daniel Dine

¹Department of Biomedical Informatics, Columbia University, New York, NY, USA. sbj2@columbia.edu

Journal of the American Medical Informatics Association : JAMIA

|October 20, 2007

Summary

This study introduces structured narrative, a novel electronic health record system that captures clinician observations as free text, enhancing data integration and reuse. This approach streamlines data entry and supports advanced clinical applications.

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Area of Science:

Health Informatics
Clinical Data Management
Natural Language Processing

Background:

Electronic health records (EHRs) often struggle with efficient capture and utilization of detailed clinical narrative.
Current systems may limit clinician expressiveness and hinder data integration for secondary uses.

Purpose of the Study:

To develop a novel EHR model, termed structured narrative, for rapid and detailed capture of clinician observations.
To enable partial structuring of narrative information for seamless integration and reuse within EHR systems.

Main Methods:

Proposed a 'structured narrative' model fusing unstructured text and coded data.
Represented clinical events as documents with marked gross and fine structures (sections, concepts, relationships).
Utilized natural language processing (NLP) for real-time coding and identification of fine structure, reducing form-based entry.

Main Results:

Validated the structured narrative model through a clinician use-case example.
Demonstrated the model's ability to represent patient information as standardized documents (templates).
Showcased real-time NLP coding of free-text entries for immediate computational use.

Conclusions:

Structured narrative facilitates data capture by allowing clinician freedom of expression.
Enables immediate feedback and supports reuse of clinical information for quality assurance and research.
Augments structured data with rich temporal, causal, and explanatory details from narrative text.