Exploring the consistency, quality and challenges in manual and automated coding of free-text diagnoses from hospital outpatient letters
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View abstract on PubMed
Summary
This summary is machine-generated.This study compared manual and automated clinical coding of diagnoses from outpatient letters. While human coders slightly outperformed automated systems, both methods showed high accuracy for single diagnoses, with automated coding deemed acceptable in 90% of cases.
Area Of Science
- Medical Informatics
- Natural Language Processing
- Clinical Terminology
Background
- Clinical coding transforms unstructured clinical text into structured data for analysis.
- Manual coding is time-consuming, driving the need for automated solutions.
- Accurate coding is vital for direct patient care, clinical communication, and research.
Purpose Of The Study
- To evaluate the quality and consistency of manual versus automated clinical coding of diagnoses.
- To compare coding performance using SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms).
- To assess automated coding acceptability in a clinical context.
Main Methods
- 100 outpatient letters were coded manually by two clinicians and automatedly using IMO's Concept Tagger.
- A clinician panel established a gold standard for a subset of diagnoses.
- Quality and consistency were evaluated using graph-based and qualitative metrics.
Main Results
- Human coders slightly outperformed automated coding when compared to the gold standard.
- Both methods performed better with single diagnoses compared to multiple.
- Automated coding was considered acceptable by clinicians in approximately 90% of instances.
Conclusions
- Automated clinical coding shows promise, approaching human performance levels.
- Further refinement may improve automated coding accuracy, especially for complex cases.
- Automated coding is a viable and largely acceptable tool for diagnosis coding in clinical settings.
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