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Towards computer-assisted coding: A case study of 'charge by documentation' software at an endoscopy clinic

HEALTH POLICY AND TECHNOLOGY 2014年第3期3卷 208-214页

作者： Jones, Kevin A. Beecroft, Nicholas J. Patterson, Emily S. Ohio State Univ Informat Technol Med Ctr Columbus OH 43210 USA Ohio State Univ Sch Hlth & Rehabil Sci Columbus OH 43210 USA

Objectives: An imminent transition to the ICD-10 diagnostic code set has increased interest in automating portions of the reimbursement process for clinical procedures. In this paper, we compare two distinct sets of billing codes generated at an endoscopy clinic using a traditional manual methods and computer-assisted coding using a 'charge by documentation' approach. Methods: This is a retrospective, cross sectional research design analyzing data collected from all patients treated at one outpatient endoscopy clinic from July 2010 through June 2011. The collected data were the medical record number, data of service, diagnosis, procedure, CPT codes, ICD-9-CM codes, and CPT modifiers. The paired data were categorized as either an exact match or discrepant. Results: 98% of the 2923 procedures were either colonoscopies or upper GI endoscopies, which have predictable workflow deviations that reliably map to changes in procedural and diagnostic codes. The codes from the two methods were an exact match for 31% of the cases. The automated approach generated 1-8 additional codes for 62% of the cases, and the manual approach generated codes without accompanying supporting documentation in the progress note for 24% of the cases. Conclusions: We conclude that the automated approach was superior to the manual approach. We recommend the 'charge by documentation' approach for settings where the workflow is relatively predictable, including pre-identified frequently occurring branches to the workflow that affect the selection of procedural and diagnostic codes. (C) 2014 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved.

关键词： ICD-9 ICD-10 CAC computer assisted coding computer assisted documentation Electronic health record

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Clustered Automated Machine Learning (CAML) model for clinical coding multi-label classification

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年第3期16卷 1507-1529页

作者： Mustafa, Akram Azghadi, Mostafa Rahimi James Cook Univ Coll Sci & Engn Townsville Qld 4811 Australia

Clinical coding is a time-consuming task that involves manually identifying and classifying patients' diseases. This task becomes even more challenging when classifying across multiple diagnoses and performing multi-label classification. Automated Machine Learning (AutoML) techniques can improve this classification process. However, no previous study has developed an AutoML-based approach for multi-label clinical coding. To address this gap, a novel approach, called Clustered Automated Machine Learning (CAML), is introduced in this paper. CAML utilizes the AutoML library Auto-Sklearn and cTAKES feature extraction method. CAML clusters binary diagnosis labels using Hamming distance and employs the AutoML library to select the best algorithm for each cluster. The effectiveness of CAML is evaluated by comparing its performance with that of the Auto-Sklearn model on five different datasets from the Medical Information Mart for Intensive Care (MIMIC III) database of reports. These datasets vary in size, label set, and related diseases. The results demonstrate that CAML outperforms Auto-Sklearn in terms of Micro F1-score and Weighted F1-score, with an overall improvement ratio of 35.15% and 40.56%, respectively. The CAML approach offers the potential to improve healthcare quality by facilitating more accurate diagnoses and treatment decisions, ultimately enhancing patient outcomes.

关键词： Machine learning AutoML Multi-label classification NLP Clinical coding computer assisted coding

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Developing an ICD-10 coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description- Based Code Selection

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JMIR FORMATIVE RESEARCH 2025年 9卷 e60095页

作者： Puts, Sander Zegers, Catharina M. L. Dekker, Andre Bermejo, Inigo Maastricht Univ GROW Res Inst Oncol & Reprod Med Ctr Dept Radiat Oncol Maastro POB 616 NL-6200 MD Maastricht Netherlands Hasselt Univ Data Sci Inst DSI Hasselt Belgium

Background: The International Classification of Diseases (ICD), developed by the World Health Organization, standardizes health condition coding to support health care policy, research, and billing, but artificial intelligence automation, while promising, still underperforms compared with human accuracy and lacks the explainability needed for adoption in medical settings. Objective: The potential of large language models for assisting medical coders in the ICD-10 coding was explored through the development of a computer-assisted coding system. This study aimed to augment human coding by initially identifying lead terms and using retrieval-augmented generation (RAG)-based methods for computer-assisted coding enhancement. Methods: The explainability dataset from the CodiEsp challenge (CodiEsp-X) was used, featuring 1000 Spanish clinical cases annotated with ICD-10 codes. A new dataset, CodiEsp-X-lead, was generated using GPT-4 to replace full-textual evidence annotations with lead term annotations. A Robustly Optimized BERT (Bidirectional Encoder Representations from Transformers) Pretraining Approach transformer model was fine-tuned for named entity recognition to extract lead terms. GPT-4 was subsequently employed to generate code descriptions from the extracted textual evidence. Using a RAG approach, ICD codes were assigned to the lead terms by querying a vector database of ICD code descriptions with OpenAI's text-embedding-ada-002 model. Results: The fine-tuned Robustly Optimized BERT Pretraining Approach achieved an overall F1-score of 0.80 for ICD lead term extraction on the new CodiEsp-X-lead dataset. GPT-4-generated code descriptions reduced retrieval failures in the RAG approach by approximately 5% for both diagnoses and procedures. However, the overall explainability F1-score for the CodiEsp-X task was limited to 0.305, significantly lower than the state-of-the-art F1-score of 0.633. The diminished performance was partly due to the reliance on code descripti

关键词： International Classification of Diseases ICD-10 computer-assisted-coding GPT-4 coding term extraction code analysis computer assisted coding transformer model artificial intelligence AI automation retrieval-augmented generation RAG large language model LLM Bidirectional Encoder Representations from Transformers Robustly Optimized BERT Pretraining Approach RoBERTa named entity recognition NER

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Automated Clinical Documentation Improvement

Automated Clinical Documentation Improvement

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Shah, Vatsal Goswami, Raxit Kumar, Vivek Shah, Binni Shah, Helly ezDI Inc Res Lab Louisville KY 40243 USA

ISBN: (纸本)9781538654880

Complete and accurate clinical documentation in the medical record has a direct impact on the assignment of codes, more accurate levels of reimbursement, and is critical to the higher quality of patient care. This paper describes the development of a system which can automatically flag the cases if there is an opportunity of improvement in patient clinical documents. Automated Clinical Documentation Improvement (CDI) leverages the natural language processing (NLP) and contextual understanding of health record structure with additional business rules logic, helping CDI specialists identify critical documentation information that may be missing from the medical record. This results in more specific coding opportunity and better understanding of the clinical complexity for accurate reimbursement. This system helped increase CDI specialists' productivity by efficiently filtering cases which need more attention from them.

关键词： Clinical documentation improvement CDI Automated CDI CA-CDI computer assisted coding Healthcare Knowledge Representation CAC NLP Clinical Decision Support

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Deep Learning for ICD coding: Looking for Medical Concepts in Clinical Documents in English and in French 9th

Deep Learning for ICD Coding: Looking for Medical Concepts i...

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9th International Conference of the Conference-and-Labs-of-the-Evaluation-Forum-Association, (CLEF)

作者： Miftahutdinov, Zulfat Tutubalina, Elena Kazan Volga Reg Fed Univ Kazan Russia Neuromation OU EE-10111 Tallinn Estonia

ISBN: (纸本)9783319989327;9783319989310

Medical Concept coding (MCD) is a crucial task in biomedical information extraction. Recent advances in neural network modeling have demonstrated its usefulness in the task of natural language processing. Modern framework of sequence-to-sequence learning that was initially used for recurrent neural networks has been shown to provide powerful solution to tasks such as Named Entity Recognition or Medical Concept coding. We have addressed the identification of clinical concepts within the International Classification of Diseases version 10 (ICD-10) in two benchmark data sets of death certificates provided for the task 1 in the CLEF eHealth shared task 2017. A proposed architecture combines ideas from recurrent neural networks and traditional text retrieval term weighting schemes. We found that our models reach accuracy of 75% and 86% as evaluated by the F-measure on the CepiDc corpus of French texts and on the CDC corpus of English texts, respectfully. The proposed models can be employed for coding electronic medical records with ICD codes including diagnosis and procedure codes.

关键词： ICD coding ICD codes Medical concept coding Medical record coding computer assisted coding Recurrent neural network Encoder-decoder model Deep learning Machine learning Death certificates CepiDc corpus CDC corpus Healthcare CLEF eHealth

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A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes

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JOURNAL OF BIOMEDICAL INFORMATICS 2020年 105卷 103418-103418页

作者： Wang, Qiong Ji, Zongcheng Wang, Jingqi Wu, Stephen Lin, Weiyan Li, Wenzhen Ke, Li Xiao, Guohong Jiang, Qing Xu, Hua Zhou, Yi Sun Yet San Univ Biomed Engn Sch Guangzhou Guangdong Peoples R China Guangzhou Med Univ Affiliated Hosp 3 Guangzhou Guangdong Peoples R China Univ Texas Hlth Sci Ctr Houston Sch Biomed Informat Houston TX 77030 USA Sun Yat San Univ Zhongshan Sch Med Dept Biomed Engn Guangzhou Guangdong Peoples R China

Objective: This study aims to develop and evaluate effective methods that can normalize diagnosis and procedure terms written by physicians to standard concepts in International Classification of Diseases (ICD) in Chinese, with the goal to facilitate automated medical coding in China. Methods: We applied the entity-linking framework to normalize Chinese diagnosis and procedure terms, which consists of two steps - candidate concept generation and candidate concept ranking. For candidate concept generation, we implemented both the traditional BM25 algorithm and an extended version that integrates a synonym knowledgebase. For candidate concept ranking, we investigated a number of different algorithms: (1) the BM25 algorithm, (2) ranking support vector machines (RankSVM), (3) a previously reported Convolutional Neural Network (CNN) approach, (4) 11 deep ranking-based methods from the MatchZoo toolkit, and (5) a new BERT (Bidirectional Encoder Representations from Transformers) based ranking method. Using two manually annotated datasets (8,547 diagnoses and 8,282 procedures) collected from a Tier 3A hospital in China, we evaluated above methods and reported their performance (i.e., accuracy) at different cutoffs. Results: The coverage of candidate concept generation was greatly improved after integrating the synonym knowledgebase, achieving 97.9% for diagnoses and 93.4% for procedures respectively. Overall the new BERT-based ranking method achieved the best performance on both diagnosis and procedure normalization, with the best accuracy of 92.1% for diagnosis and 80.1% for procedure, when the top one concept and exact match criteria were used. Conclusions: This study developed and compared diverse entity-linking methods to normalize clinical terms in Chinese and our evaluation shows good performance on mapping disease terms to ICD codes, demonstrating the feasibility of automated encoding of clinical terms in Chinese.

关键词： computer assisted coding ICD encoding Entity-linking

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Automated Clinical Documentation Improvement

Automated Clinical Documentation Improvement

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Vatsal Shah Raxit Goswami Vivek Kumar Binni Shah Helly Shah Research Lab ezDI Inc.

ISBN: (纸本)9781538654897

关键词： Clinical documentation improvement CDI Automated CDI CA-CDI computer assisted coding Healthcare knowledge representation CAC NLP Clinical decision support Capacitive Discharge Ignition SLC25A20 gene Clinical Decision Support Natural Language Processing NINL gene Documentation Medical Records Medical

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