Comparison of Validity of Mapping between Drug Indications and ICD-10Direct and Indirect Terminology Based Approaches
04 May 2013
accepted: 28 February 2014
20 January 2018 (online)
Background: Mapping of drug indications to ICD-10 was undertaken in Korea by a public and a private institution for their own purposes. A different mapping approach was used by each institution, which presented a good opportunity to compare the validity of the two approaches.
Objectives: This study was undertaken to compare the validity of a direct mapping approach and an indirect terminology based mapping approach of drug indications against the gold standard drawn from the results of the two mapping processes.
Methods: Three hundred and seventy-five cardiovascular reference drugs were selected from all listed cardiovascular drugs for the study. In the direct approach, two experienced nurse coders mapped the free text indications directly to ICD-10. In the indirect terminology based approach, the indications were extracted and coded in the Korean Standard Terminology of Medicine. These terminology coded indications were then manually mapped to ICD-10. The results of the two approaches were compared to the gold standard. A kappa statistic was calculated to see the compatibility of both mapping approaches. Recall, precision and F1 score of each mapping approach were calculated and analyzed using a paired t-test.
Results: The mean number of indications for the study drugs was 5.42. The mean number of ICD-10 codes that matched in direct approach was 46.32 and that of indirect terminology based approach was 56.94. The agreement of the mapping results between the two approaches were poor (kappa = 0.19). The indirect terminology based approach showed higher recall (86.78%) than direct approach (p < 0.001). However, there was no difference in precision and F1 score between the two approaches.
Conclusions: Considering no differences in the F1 scores, both approaches may be used in practice for mapping drug indications to ICD-10. However, in terms of consistency, time and manpower, better results are expected from the indirect terminology based approach.
- 1 Fung KW, Bodenreider O. editors Utilizing the UMLS for semantic mapping between terminologies. AMIA 2005 Annual Symposium; 2005 October. Washington, DC. Maryland: AMIA; 2005: 22-26.
- 2 Fung KW, Bodenreider O, Aronson AR, Hole WT, Srinivasan S. Combining lexical and semantic methods of inter-terminology mapping using the UMLS. Studies in health technology and informatics 2007; 12 0Pt (01) 605-609.
- 3 Zhou L, Plasek JM, Mahoney LM, Chang F, DiMaggio D, Rocha RA. Mapping Partners Master Drug Dictionary to RxNorm using an NLP- based approach. J Biomed Inform 2011; 45 (04) 626-633.
- 4 Sherertz D, Tuttle M, Blois M, Erlbaum M. editors Intervocabulary mapping within the UMLS: the role of lexical matching. The Twelfth Annual Symposium on Computer Applications in Medical Care; 1988 November. Washington, DC. Washington, DC: IEEE Computer Society Press; 1988: 6-9.
- 5 Barrows RC, Cimino Jr. JJ, Clayton PD. editors. Mapping clinically useful terminology to a controlled medical vocabulary. Eighteenth Annual Symposium on Computer Applications in Medical Care. November Washington, DC. Washington, DC: AMIA; 1994. -9. 5 2247832
- 6 Dolin RH, Huff SM, Rocha RA, Spackman KA, Campbell KE. Evaluation of a “lexically assign, logically refine” strategy for semi-automated integration of overlapping terminologies. Journal of the American Medical Informatics Association. JAMIA 1998; 5 (02) 203-213. Epub 1998/04/03. PubMed PMID: 9524353; PubMed Central PMCID: PMC61291
- 7 Fung KW, Jao CS, Demner-Fushman D. Extracting drug indication information from structured product labels using natural language processing. Journal of the American Medical Informatics Association. JAMIA 2013; 20 (03) 482-488. Epub 2013/03/12. PubMed PMID: 23475786; PubMed Central PMCID: PMC3628062
- 8 Rosenbloom ST, Brown SH, Froehling D, Bauer BA, Wahner-Roedler DL, Gregg WM. et al Using SNOMED CT to represent two interface terminologies. Journal of the American Medical Informatics Association. JAMIA 2009; 16 (01) 81-88.
- 9 Park Y-T, Yoon J-S. Speedie SM, Yoon H, Lee J. Health Insurance Claim Review Using Information Technologies. Healthc Inform Res 2012; 18 (03) 215-224.
- 10 Chen DT, Wynia MK, Moloney RM, Alexander GC. US physician knowledge of the FDA‐approved indications and evidence base for commonly prescribed drugs: results of a national survey. Pharmacoepidemiol Drug Saf 2009; 18 (11) 1094-1100.
- 11 Carter JS, Brown SH, Erlbaum MS, Gregg W, Elkin PL, Speroff T. et al. editors Initializing the VA medication reference terminology using UMLS metathesaurus co-occurrences. AMIA 2002 Annual Symposium. 2002. November San Antonio, TX. Philadelphia: Hanley & Belfus, Inc; 9-13.
- 12 Burton MM, Simonaitis L, Schadow G. editors Medication and indication linkage: A practical therapy for the problem list? AMIA 2008 Annual Symposium; 2008 November. Washington, DC. Washington, DC: AMIA; 2008: 8-12.
- 13 Duclos C, Venot A. Structured representation of drug indications: lexical and semantic analysis and object-oriented modeling. Methods Inf Med 2000; 39 (01) 83-87. Epub 2000/04/29. PubMed PMID: 10786076
- 14 Ji S, Matsumura Y, Kuwata S, Nakano H, Chen Y, Teratani T. et al Creation of a master table for checking indication and contraindication of medicine from a knowledge base linked with a thesaurus. J Med Syst 2004; 28 (06) 561-573. Epub 2004/12/24. PubMed PMID: 15615284
- 15 Dorland’s Illustrated Medical Dictionary. 28th edition. Philadelphia: Elsevier Health Sciences; 1994
- 16 NLM. Unified Medical Language System and reg.
- 17 KOSTOM Ver2011_Q2 (Internet). Korea Health and welfare Information Service; 2011. Available from. http://www.medistds.or.kr/
- 18 Yang Y, Chute C. editors An application of least squares fit mapping to clinical classification. Sixteenth Annual Symposium on Computer Applications in Medical Care. Baltimore, MD. New York: McGraw-Hill, Inc.; 1992
- 19 Richesson RL. A process for achieving comparable data from heterogeneous databases (Dissertation). Houston: The University of Texas School of Health Information Sciences. 2003
- 20 Pereira S, Neveol A, Massari P, Joubert M, Darmoni S. Construction of a semi-automated ICD-10 coding help system to optimize medical and economic coding. Studies in health technology and informatics 2006; 124: 845-850. PubMed PMID: 17108618
- 21 Buemi A, Boisvert M, Côté R. Transcodage SNOMED-CIM-10: proposition pour une meilleure indexation des dossiers médicaux. Actes JFIM2002.
- 22 Huff SM. Ontologies, vocabularies, and data models. In. Greenes RA. editor Clinical decision support: the road ahead. Boston: Elsevier Academic Press; 2007: 307-324.
- 23 Rosenbloom ST, Miller RA, Johnson KB, Elkin PL, Brown SH. Interface terminologies: facilitating direct entry of clinical data into electronic health record systems. Journal of the American Medical Informatics Association. JAMIA 2006; 13 (03) 277-288.