J Korean Soc Med Inform.  2009 Mar;15(1):141-151.

Toward the Automatic Generation of the Entry Level CDA Documents

• Jung S
• Kim S
• Yoo S
• Choi J

Abstract

OBJECTIVE
CDA (Clinical Document Architecture) is a markup standard for clinical document exchange. In order to increase the semantic interoperability of documents exchange, the clinical statements in the narrative blocks should be encoded with code values. Natural language processing (NLP) is required in order to transform the narrative blocks into the coded elements in the level 3 CDA documents. In this paper, we evaluate the accuracy of text mapping methods which are based on NLP.
METHODS
We analyzed about one thousand discharge summaries to know their characteristics and focused the syntactic patterns of the diagnostic sections in the discharge summaries. According to the patterns, different rules were applied for matching code values of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT).
RESULTS
The accuracy of matching was evaluated using five-hundred discharge summaries. The precision was as follows: 86.5% for diagnosis, 61.8% for chief complaint, 62.7%, for problem list, and 64.8% for discharge medication.
CONCLUSION
The text processing method based on the pattern analysis of a clinical statement can be effectively used for generating CDA entries.

Keyword

Clinical Document Architecture; Natural Language Processing; SNOMED CT

MeSH Terms

Diagnosis
Natural Language Processing
Semantics
Systematized Nomenclature of Medicine

Figure

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  Figure 1 Main components of CDA Studio®

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  Figure 2 A sample example of a discharge medication section

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  Figure 3 A sample example of an observation impression section

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  Figure 4 Database for SNOMED CT

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  Figure 5 Three stages of mapping process

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  Figure 6 Steps of preprocessing mapping

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  Figure 7 Steps of text processing mapping

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  Figure 8 Architecture of Entry Mapper

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  Figure 9 An example of CDA document with entries for semi-structured section (diagnosis section)

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