Europe PMC (PubMed Central) · 2018 · 40 citations · 16 references
Open access
EngineeringDigital PathologyDiagnosisComputer-assisted Diagnostic CodingDisease ClassificationSnomed CtHospital MedicineNatural Language ProcessingMedical CodingAi HealthcareDisease DiagnosisIcd-10 MappingsRadiologyClinical LanguageMedical ImagingDiagnostic CodesMedical Language ProcessingMedical Image ComputingClinical DataMedical Information SystemPatient SafetyComputer-aided DiagnosisMedicineClinical Decision Support SystemHealth InformaticsEmergency Medicine
Computer‑assisted diagnostic coding aims to improve operational productivity and accuracy of clinical coders, yet accuracy for complex and less prevalent cases remains an open research problem. This study evaluates the effectiveness of using SNOMED CT for ICD‑10 diagnosis coding across a broad spectrum of diagnostic codes. Hospital progress notes provided narrative‑rich electronic patient records, and a natural language processing approach guided coding via mappings between SNOMED CT and ICD‑10‑AM. The approach achieved 54.1% sensitivity and 70.2% positive predictive value, a promising result given the task complexity and the simplicity of the method compared to manual validation studies.
Computer-assisted (diagnostic) coding (CAC) aims to improve the operational productivity and accuracy of clinical coders. The level of accuracy, especially for a wide range of complex and less prevalent clinical cases, remains an open research problem. This study investigates this problem on a broad spectrum of diagnostic codes and, in particular, investigates the effectiveness of utilising SNOMED CT for ICD-10 diagnosis coding. Hospital progress notes were used to provide the narrative rich electronic patient records for the investigation. A natural language processing (NLP) approach using mappings between SNOMED CT and ICD-10-AM (Australian Modification) was used to guide the coding. The proposed approach achieved 54.1% sensitivity and 70.2% positive predictive value. Given the complexity of the task, this was encouraging given the simplicity of the approach and what was projected as possible from a manual diagnosis code validation study (76.3% sensitivity). The results show the potential for advanced NLP-based approaches that leverage SNOMED CT to ICD-10 mapping for hospital in-patient coding.
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