Validation of a self-supervised architecture for automated ICF coding in electronic health records

Abstract

Background: The International Classification of Functioning, Disability and Health (ICF) provides a comprehensive framework for assessing health beyond disease-centric models, yet its integration into clinical practice remains limited. Key challenges include the complexity of ICF coding, the lack of standardized implementation in electronic health records, and the prevalence of unstructured health data. This study aims to validate an algorithm for automatically converting unstructured health record texts into ICF codes. Using self-supervised learning methods, this research aims to improve ICF utilization while ensuring compliance with data regulations. Results: The analysis dataset included 151 electronic healthcare documents from different healthcare professionals, including physicians, nurses, therapists, social workers and rehabilitation counsellors. The algorithm performed equally well on texts from different professionals. The overall performance on the analysis dataset was 0.94 for precision and 0.88 for recall, resulting in an F1 score of 0.91. Conclusions: The results of this validity study demonstrate the algorithm's strong performance in automatically generating ICF codes from free-text clinical notes. Integration of the algorithm into electronic health records systems has the potential to enable automated ICF coding, producing structured data on functioning, disability, and health. This supports standardized reporting, enhances health system efficiency, and facilitates more personalized care. Future research should investigate the scalability, interoperability, and cross-cultural validation of the algorithm.