ICD-10-CM Crosswalks in the primary care setting: assessing reliability of the GEMs and reimbursement mappings. (7th February 2015)

• Record Type: Journal Article
• Authors: Turer, Robert W.; Zuckowsky, Theresa D.; Causey, H. Jennifer; Rosenbloom, S. Trent
• Is Part Of: Journal of the American Medical Informatics Association. Volume 22:Number 2(2015:Mar.)
• Page Start: 417
• Page End: 425
• Publication Date: 2015-02-07
• Subjects: ICD-9-CM -- ICD-10-CM -- GEMs -- General Equivalence Mappings -- Reimbursement Mappings -- International Classification of Diseases; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocu028
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective The general equivalence mappings (GEMs) and reimbursement mappings (RMs) facilitate translation between ICD-9-CM and ICD-10-CM. This study compared prospectively dual-encoded diagnoses assigned by professional coders with the GEMs/RMs in a clinical setting. Materials and Methods Professional coders manually encoded diagnoses from 100 primary care notes into both ICD-9-CM and ICD-10-CM. The investigators evaluated whether manual mappings were reproducible using the GEMs/RMs. Reproducible mappings with one ICD-9-CM and one ICD-10-CM code ("one-to-one") were classified as exact or approximate using GEMs flags. Mismatches were characterized manually. Results Manual encodings were reproducible from the forward GEMs, backward GEMs, and RMs in 85.2%, 90.4%, and 88.1% of diagnoses, respectively. For one-to-one, reproducible mappings, 61% (forward) and 63% (backward) were approximate mappings compared to 85% and 95% in the GEMs as a whole. Mismatches between manual and GEMs encodings were due to differences in coder interpretation (11%–13%), subtle hierarchical differences (52%–55%), or unknown reasons (32%–35%). Discussion This study highlights inconsistencies between manual encoding and using the GEMs/RMs. The number of approximate mappings in our population compared to all one-to-one GEMs entries supports the notion that statistics describing the GEMs as a whole might not represent the most important mappings for each organization. The mismatch characteristics highlight the subtle differences between manual encoding and using the GEMs/RMs. Conclusion These results support the need for organizations to assess the GEMs and RMs in their own environment to avoid changes in reimbursement and longitudinal statistics.