Developing a Reference Framework for Cross-Mapping between Different Dental Diagnostic Terminologies

Megan Vesel
Megan Vesel
Northern Michigan University

Vesel MK, Shimpi NA, Acharya A.
Biomedical Informatics Research Center

Research Area: Dental Informatics 

Background: Unlike in medicine, there are no nationally standardized diagnostic terminology sets (DTS) for dentistry. International Classification of Disease (ICD) codes do cover some of the diagnoses in dentistry; however it is not granular enough. A more granular, dental-specific DTS, EZCodes, was created by Harvard University. American Dental Association has also developed a DTS called SNODENT for dentistry. Our objective was to develop a reference framework between ICD and EZCodes and to understand the coverage of the diagnostic terms across these DTSs.

Methods: Cross-mapping was achieved by looking for semantic and/or syntactic matching terms between ICD-9-to-ICD-10, ICD-9-to-EZCodes, and ICD-10-to-EZCodes. Mapping between each term was coded as complete, partial or no match. Once all terms were mapped between two DTSs, we categorized the type of relationship as one-to-one, one-to-many, or many-to-one. All mapping was done using online references, and searching for direct or related matches. Cross-mapping was then reviewed by a dentist for accuracy.

Results: Overall, ICD-9-to-EZCodes showed 36% of the terms matched, of these, 35% completely matched, 65% partially matched. ICD-10-to-EZCodes had a greater amount of matching codes at 47%, out of which 74% completely matched, and 26% partially matched. ICD-9-to-ICD-10 showed 95% matching, with 70% complete and 30% partial matches.

Conclusions: This study developed a reference framework for cross-mapping between different dental diagnostic terminologies. Our results confirm the ICD-9-to-ICD-10 cross-mapping had the largest amount of complete and partial matches and lowest amount of no matches, as expected when comparing related terminologies. Being a detailed dental diagnostic vocabulary, EZCodes had the largest percentage of no matches to ICD-9 and ICD-10. Additionally, ICD-10 was more in depth than ICD-9, explaining why ICD-10 more closely matched the granularity of EZCodes, with fewer no matches in ICD-10-to-EZCodes than in ICD-9-to-EZCodes.