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Identifying inconsistencies in SNOMED CT problem lists using structural indicators.

Ankur Agrawal¹, Yehoshua Perl², Yan Chen³

¹Manhattan College, Riverdale, NY;

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 20, 2014

Summary

This summary is machine-generated.

This study introduces methods using concept complexity to find errors in problem lists. These techniques can identify many potential inconsistencies in clinical data, highlighting the need for improved quality assurance.

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Area of Science:

Medical Informatics
Health Data Standards
Clinical Terminology

Background:

The National Library of Medicine published CORE and VA/KP problem lists to support SNOMED CT encoding in electronic health records.
Accurate and consistent problem list content is crucial for reliable patient data and clinical decision-making.

Purpose of the Study:

To evaluate the effectiveness of structural indicators, specifically concept word length and number of parents, in identifying inconsistencies within problem lists.
To present a novel structural indicator for detecting errors in concept synonyms.

Main Methods:

Assessed concept word length and the number of parent concepts as metrics for identifying complex and potentially erroneous concepts.
Quantified the method's ability to isolate concepts with a high probability of error (over 40%).
Developed and evaluated a structural indicator for identifying errors in concept synonyms, achieving a 52% detection rate.

Main Results:

The implemented methods successfully identified concepts with a high likelihood of containing errors.
Over 40% of isolated concepts were found to be potentially erroneous based on structural complexity.
A specific structural indicator identified errors in synonyms for 52% of the examined concepts.

Conclusions:

Problem lists contain inconsistencies that impact data quality.
Structural indicators are effective tools for identifying potential errors in SNOMED CT-encoded problem lists.
Further quality assurance measures are essential to enhance the reliability of clinical terminology and patient data.