Clinical coding support based on structured data stored in electronic health records

Clinical coding is an increasingly essential process within health organizations, usually performed manually and entailing several challenges: its administrative burden, raising costs and eventual errors. To address this issue, several coding support systems have been proposed across the literature. However, these systems are based on text processing methods that may be limited by poor text quality, ambiguity and lack of annotated resources. As electronic health record systems tend to implement more structured data formats, we propose a methodology for coding support based on structured clinical data collected during inpatient care from a semi-structured electronic health record. We follow a statistical learning paradigm and investigate several building blocks of the methodology to assess the feasibility of the approach. We present and discuss preliminary results obtained with real data extracted from an Internal Medicine department and identify several measures to further develop the methodology, model performance and generalizability.