Distributed Knowledge Based Clinical Auto-Coding System

Abstract

Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD-10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously.

A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries

“…Firstly, there are a few additional ICD-10-AM codes that are more specific (approximately 4, 915 codes) and are coded in Australia along with 15 other countries that use Australian classification system as their national classification system. For example, in the US system, 'contact with venomous spiders' is coded as 21, whereas in Australia, there is more specificity by adding fourth character level in identifying the type of the spider [38].…”

“…There are several reasons behind the wrong assignment of codes. First, the assignment of ICD codes to a patient's records could be erroneous due to the subjective nature of human perception [38]. Second, the manual process of assigning codes is a tedious task which leads to inability to locate critical and subtle findings due to fatigue.…”

Consultation analysis: use of free text versus coded text

“…Much research on coding has been done in hospitals, where clinical coders are used, and considered to be expensive, inefficient and prone to error [7]. In consequence algorithms have been created to reduce costs and automatize the process [8] but taking into account "No free lunch" theorems [9] they cannot be the answer. As stated by Wolpert "For any two learning algorithms A and B […] there are just as many situations (appropriately weighted) in which algorithm A is superior to algorithm B as vice versa" [9].…”