Computational recognition of SNOMED CT codes from ED case notes.
This paper is a study of patient notes from an emergency department and a determination of their consistency for correct SNOMED CT encoding of the diagnosis, and comparison with a clinical language processing (CLP) system that determined the SCT diagnoses. Three set of notes were reviewed by a clinician with 500 records in each where: the clinician and CLP codes where the same (Matched Set); where they were different (Unmatched Set); and, where the clinicians had failed to enter a code (Unassigned Set) giving accuracies of 75%, 33.4% and 44.9% respectively.
School of IT, Sydney University.
Jon
J
Patrick
Dung
D
Nguyen
Tingxin
T
Wang
Richard
R
Paoloni
D004636:Emergency Service, Hospital; D006801:Humans; D016347:Medical Records Systems, Computerized / Q000592:standards*; D039061:Systematized Nomenclature of Medicine