Assisted morbidity coding: the SISCO.web use case for identifying the main diagnosis in Hospital Discharge Records

TL;DR

This paper presents SISCO.web, a web-based tool that uses NLP, coding rules, and decision trees to assist physicians in accurately identifying the main diagnosis in Hospital Discharge Records, improving coding reproducibility.

Contribution

The paper introduces SISCO.web, a novel system that supports medical professionals in coding diagnoses more accurately using AI and rule-based algorithms.

Findings

Promising accuracy in ICD coding suggestions

Effective support for physicians in diagnosis coding

Improved reproducibility of clinical coding

Abstract

Coding morbidity data using international standard diagnostic classifications is increasingly important and still challenging. Clinical coders and physicians assign codes to patient episodes based on their interpretation of case notes or electronic patient records. Therefore, accurate coding relies on the legibility of case notes and the coders' understanding of medical terminology. During the last ten years, many studies have shown poor reproducibility of clinical coding, even recently, with the application of Artificial Intelligence-based models. Given this context, the paper aims to present the SISCO.web approach designed to support physicians in filling in Hospital Discharge Records with proper diagnoses and procedures codes using the International Classification of Diseases (9th and 10th), and, above all, in identifying the main pathological condition. The web service leverages NLP algorithms, specific coding rules, as well as ad hoc decision trees to identify the main condition, showing promising results in providing accurate ICD coding suggestions.