Using SNOMED-CT to encode summary level data - a corpus analysis.
Extracting and encoding clinical information captured in free text with standard medical terminologies is vital to enable secondary use of electronic medical records (EMRs) for clinical decision support, improved patient safety, and clinical/translational research. A critical portion of free text is comprised of 'summary level' information in the form of problem lists, diagnoses and reasons of visit. We conducted a systematic analysis of SNOMED-CT in representing the summary level information utilizing a large collection of summary level data in the form of itemized entries. Results indicate that about 80% of the entries can be encoded with SNOMED-CT normalized phrases. When tolerating one unmapped token, 96% of the itemized entries can be encoded with SNOMED-CT concepts. The study provides a solid foundation for developing an automated system to encode summary level data using SNOMED-CT.
Department of Health Sciences Research Mayo Clinic College of Medicine, Rochester, MN.
Hongfang
H
Liu
Kavishwar
K
Wagholikar
Stephen Tze-Inn
ST
Wu