Distributed knowledge based clinical auto-coding system

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

Voice-controlled clinical coding companion (VC4) for ICD-10-AM and ACHI code assignment

The use of voice assistant devices such as Siri, Alexa, Google Assistant, and Samsung Bixby in the healthcare industry is gaining a lot of attention nowadays. Many healthcare professionals believe that a clinically-oriented Voice assistant devices will ease the burden of accessing patient’s information, maintaining documents, and improve the patient experience. This paper presents a prototypic implementation of Alexa, named VC4 (voice controlled clinical coding companion), that can assist clinical coders by suggesting most appropriate ICD-10-AM and ACHI codes for a given clinical description through voice commands

Will auto-coding be a reality anytime soon?

Clinical coding is carried out in hospitals to support statistical analysis of clinical data that leads to funding, insurance claims processing and research. Ever expanding and changing clinical classification systems such as ICD-10-AM and ACHI, challenges in the healthcare industry are increased due to increasing set of codes, the complexity of manual code assignment, and extensive training and recruitment costs. The use of Natural Language Processing (NLP) and Machine learning (ML) techniques for computer-assisted coding or auto-coding is considered as a possible solution to overcome the problems of manual coding. This perception is questioned in this work, by carrying out experimental tests on a selected set of NLP and ML techniques, using 190 discharge summaries related to diseases of respiratory and gastrointestinal systems. The results indicate that accuracy of auto-coding ranges between 40% to 79% depending on the computational techniques used. The paper concludes that without human involvement, auto-coding would not be a reality in the current healthcare data environment

Distributed knowledge-based computer-assisted clinical coding system

Free-text clinical narratives recorded during clinical care are used for secondary purposes such as clinical coding and subsequent statistical analysis. The current process of manual clinical coding is time-consuming, expensive and error prone. Computer-Assisted Coding (CAC) systems have the potential to expedite this process. We aim to develop a distributed knowledge-based computer-assisted clinical coding system that can semi-automate the process of clinical coding. This paper, describes the initial phase of our work which currently uses the pattern matching method to assign ICD-10-AM and ACHI codes

Comparative evaluation of accuracy of selected machine learning classification techniques for diagnosis of cancer : a data mining approach

With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers

Comparative analysis of algorithmic approaches for auto-coding with ICD-10-AM and ACHI

Clinical coding is done using ICD-10-AM (International Classification of Diseases, version 10, Australian Modification) and ACHI (Australian Classification of Health Interventions) in acute and sub-acute hospitals in Australia for funding, insurance claims processing and research. The task of assigning a code to an episode of care is a manual process. This has posed challenges due to increase set of codes, the complexity of care episodes, and large training and recruitment costs of clinical coders. Use of Natural Language Processing (NLP) and Machine Learning (ML) techniques is considered as a solution to this problem. This paper carries out a comparative analysis on a selected set of NLP and ML techniques to identify the most efficient algorithm for clinical coding based on a set of standard metrics: precision, recall, F-score, accuracy, Hamming loss and Jaccard similarity

AI-based ICD coding and classification approaches using discharge summaries : a systematic literature review

The assignment of codes to free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. The current scenario of assigning clinical codes is a manual process which is very expensive, time-consuming and error prone. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related machine learning and deep learning methods and techniques to resolve the problem of manual coding of clinical narratives and to assist human coders to assign clinical codes more accurately and efficiently. The main objective of this systematic literature review is to provide a comprehensive overview of automated clinical coding systems that utilise appropriate NLP, machine learning and deep learning methods and techniques to assign the International Classification of Diseases (ICD) codes to discharge summaries. We have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and conducted a comprehensive search of publications from January, 2010 to December 2021 in four high quality academic databases: PubMed, ScienceDirect, Association for Computing Machinery (ACM) Digital Library, and the Association for Computational Linguistics (ACL) Anthology. We reviewed 6128 publications; 42 met the inclusion criteria. This review identified: 6 datasets having discharge summaries (2 publicly available, 4 acquired from hospitals); 14 NLP techniques along with some other data extraction processes, different feature extraction and embedding techniques. The review also shows that there is a significant increase in the use of deep learning models compared to machine learning. To measure the performance of classification methods, different evaluation metrics are used. Efforts are still required to improve ICD code prediction accuracy, availability of large-scale de-identified clinical corpora with the latest version of the classification system. This can be a platform to guide and share knowledge with the less experienced coders and researchers