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Coding of unstructured clinical free-text to produce interoperable structureddata is essential to improve direct care, support clinical communication and toenable clinical research.However, manual clinical coding is difficult and timeconsuming, which motivates the development and use of natural languageprocessing for automated coding. This work evaluates the quality andconsistency of both manual and automated clinical coding of diagnoses fromhospital outpatient letters. Using 100 randomly selected letters, two humanclinicians performed coding of diagnosis lists to SNOMED CT. Automated codingwas also performed using IMO's Concept Tagger. A gold standard was constructedby a panel of clinicians from a subset of the annotated diagnoses. This wasused to evaluate the quality and consistency of both manual and automatedcoding via (1) a distance-based metric, treating SNOMED CT as a graph, and (2)a qualitative metric agreed upon by the panel of clinicians. Correlationbetween the two metrics was also evaluated. Comparing human andcomputer-generated codes to the gold standard, the results indicate that humansslightly out-performed automated coding, while both performed notably betterwhen there was only a single diagnosis contained in the free-text description.Automated coding was considered acceptable by the panel of clinicians inapproximately 90% of cases.

Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters