Auto-mapping Clinical Documents to ICD-10 Using SNOMED-CT

Excessive paperwork is a considerable issue that leads to additional burdens for health-care professionals. In Thai health-care systems, physicians manually review medical records to select an appropriate principle diagnosis and other co-morbidities and convert them into ICD-10s to claim financial support from the government. Accordingly, 160,000 ICD-10 codes and 46,000 in-patient discharge summaries are documented by physicians at Maharaj Nakorn Chiang Mai hospital each year. As a result, to decrease physicians' burden of manual paper-work, we created a new approach to automatically analyse discharge summary notes and map the diagnoses to ICD-10s. We combined SNOMED-CT and natural language processing techniques within the approach through 3 steps: cleaning data; extracting keywords from discharge summary notes; and matching keywords to ICD-10. In this paper, we present that mapping clinical documents by using approximate matching and SNOMED-CT shows potential to be used for automating the ICD-10 mapping process.