Intelligent audit code generation from free text in the context of neurosurgery

TL;DR

This paper presents a semi-automated method for extracting structured audit codes from free-text clinical notes in neurosurgery, combining rule-based and dictionary-based approaches to improve data quality and efficiency.

Contribution

It introduces a novel semi-automated approach tailored for neurosurgical clinical notes, integrating specialist rules with dictionary matching for better information extraction.

Findings

Effective extraction of audit codes from neurosurgical notes

Improved accuracy over purely manual coding methods

Potential for application in other specialized medical domains

Abstract

Clinical auditing requires codified data for aggregation and analysis of patterns. However in the medical domain obtaining structured data can be difficult as the most natural, expressive and comprehensive way to record a clinical encounter is through natural language. The task of creating structured data from naturally expressed information is known as information extraction. Specialised areas of medicine use their own language and data structures; the translation process has unique challenges, and often requires a fresh approach. This research is devoted to creating a novel semi-automated method for generating codified auditing data from clinical notes recorded in a neurosurgical department in an Australian teaching hospital. The method encapsulates specialist knowledge in rules that instantaneously make precise decisions for the majority of the matches, followed up by dictionary-based matching of the remaining text.