Using SNOMED CT-encoded problems to improve ICD-10-CM coding—A randomized controlled experiment. (June 2019)

• Record Type: Journal Article
• Authors: Fung, Kin Wah; Xu, Julia; Rosenbloom, S. Trent; Campbell, James R.
• Is Part Of: International journal of medical informatics. Volume 126(2019)
• Page Start: 19
• Page End: 25
• Publication Date: 2019-06
• Subjects: SNOMED CT -- ICD-10-CM -- Inter-terminology mapping -- Administrative codes -- Coding quality; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2019.03.002
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: ICD-10-CM coding quality has been called into question. SNOMED CT encoded clinical problems in the EHR can be used to assist ICD-10-CM coding. Map-assisted coding reduces coding time and can potentially improve coding reliability and accuracy for experience coders. Abstract: Objective: Clinical problems in the Electronic Health Record that are encoded in SNOMED CT can be translated into ICD-10-CM codes through the NLM's SNOMED CT to ICD-10-CM map (NLM Map). This study evaluates the potential benefits of using the map-generated codes to assist manual ICD-10-CM coding. Methods: De-identified clinic notes taken by the physician during an outpatient encounter were made available on a secure web server and randomly assigned for coding by professional coders with usual coding or map-assisted coding. Map-assisted coding made use of the problem list maintained by the physician and the NLM Map to suggest candidate ICD-10-CM codes to the coder. A gold standard set of codes for each note was established by the coders using a Delphi consensus process. Outcomes included coding time, coding reliability as measured by the Jaccard coefficients between codes from two coders with the same method of coding, and coding accuracy as measured by recall, precision and F-score according to the gold standard. Results: With map-assisted coding, the average coding time per note reduced by 1.5 min (p = 0.006). There was a small increase in coding reliability and accuracy (not statistical significant). The benefits were more pronounced in the more experienced than less experienced coders. Detailed analysis of cases in which the correct ICD-10-CM codes were not found by the NLM Map showed that most failures were related to omission in the problem list and suboptimal mapping of the problem list terms to SNOMED CT. Only 12% of the failures was caused by errors in the NLM Map. Conclusion: Map-assisted coding reduces coding time and can potentially improve coding reliability and accuracy, especially for more experienced coders. More effort is needed to improve the accuracy of the map-suggested ICD-10-CM codes.