ArticleFrontiers in digital health2026
Opportunities and challenges in automated coding of electronic health records: a pilot study for rare disease registries.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate medical coding is essential for disease registries, particularly in the context of rare conditions. Manually transforming electronic health records data into standardized codes is time-consuming and resource intensive. This study evaluates an automated coding system using synthetic health records data and explores the potential benefits and challenges of introducing this tool into rare diseases registries activities. We developed a hybrid architecture combining a symbolic component with medical knowledge graphs and an ensemble of three widely used Large Language Models with a critical review mechanism. Ninety-nine synthetic Italian-language clinical reports were coded by the system. Subsequently, a multidisciplinary expert panel performed a double-coding validation of extracted terms, categorizing automated results into four groups: correct, incorrect, inaccurate, or missing codes. The system extracted a total of 479 terms (264 diagnosis codes and 215 procedure codes) mapped to ICD-9-CM classification. The expert panel, considered as the gold standard, identified 500 terms (302 diagnosis codes and 198 procedure codes). Chi-square analysis highlighted statistically significant differences between diagnosis and procedure coding in at least one of the four groups of results (
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