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Aligning Structured and Unstructured Medical Problems Using UMLS.

Lorena Carlo¹, Herbert S Chase, Chunhua Weng

¹Department of Biomedical Informatics, Columbia University, New York, NY, 10032.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 25, 2011

Summary

This summary is machine-generated.

This study aligned medical problems in electronic health records (EHR) using UMLS. Automatic alignment achieved 23.8% overlap, highlighting challenges in integrating structured and unstructured EHR data.

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Area of Science:

Medical Informatics
Natural Language Processing
Electronic Health Records

Background:

Integrating structured and unstructured data in Electronic Health Records (EHR) is crucial for comprehensive patient information.
Existing methods for aligning medical problems across different EHR data formats face challenges.

Purpose of the Study:

To pilot a method for aligning medical problems between structured (ICD-9 diagnoses) and unstructured (discharge summaries) EHR data.
To evaluate the accuracy of an automated alignment approach using the Unified Medical Language System (UMLS).

Main Methods:

Utilized Natural Language Processing (NLP) software (MedLEE) to extract 120 medical problems from discharge summaries.
Aligned extracted problems with 87 ICD-9 diagnoses from 19 hospital visits across 4 patients.
Assessed alignment accuracy through manual review by a medical doctor.

Main Results:

The automatic alignment method achieved an average overlap of 23.8% between structured and unstructured EHR data.
This accuracy was approximately half of the 43.56% overlap achieved through manual review.
Demonstrated variability in aligning medical problems between the two data sources.

Conclusions:

Automated alignment of medical problems in EHR data using UMLS shows potential but requires further refinement.
The study highlights the complexities and limitations in current approaches to EHR data integration.
Further research is needed to improve the accuracy and efficiency of integrating structured and unstructured EHR information.