Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4

2025-12-04

AI Summary

Clinicians’ manual documentation of clinical notes is time-consuming, impeding diagnostic efficiency and patient experience. To address this, we propose a domain-informed, prompt-engineered method for automated clinical note generation: leveraging ICD-10 codes as input, we integrate a clinical ontology knowledge graph to enhance semantic understanding, and combine semantic retrieval with Chain-of-Thought prompting to guide GPT-4 in producing high-quality, professional, and structured notes. Our approach significantly improves medical accuracy, logical coherence, and clinical plausibility. Evaluations on six real-world clinical cases from the CodiEsp test set demonstrate substantial improvements over standard single-shot prompting—particularly in clinical professionalism, information completeness, and consistency with clinical practice. The method effectively reduces clinicians’ documentation burden and shows strong potential for clinical deployment.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

In the past decade a surge in the amount of electronic health record (EHR) data in the United States, attributed to a favorable policy environment created by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and the 21st Century Cures Act of 2016. Clinical notes for patients' assessments, diagnoses, and treatments are captured in these EHRs in free-form text by physicians, who spend a considerable amount of time entering and editing them. Manually writing clinical notes takes a considerable amount of a doctor's valuable time, increasing the patient's waiting time and possibly delaying diagnoses. Large language models (LLMs) possess the ability to generate news articles that closely resemble human-written ones. We investigate the usage of Chain-of-Thought (CoT) prompt engineering to improve the LLM's response in clinical note generation. In our prompts, we use as input International Classification of Diseases (ICD) codes and basic patient information. We investigate a strategy that combines the traditional CoT with semantic search results to improve the quality of generated clinical notes. Additionally, we infuse a knowledge graph (KG) built from clinical ontology to further enrich the domain-specific knowledge of generated clinical notes. We test our prompting technique on six clinical cases from the CodiEsp test dataset using GPT-4 and our results show that it outperformed the clinical notes generated by standard one-shot prompts.

Problem

• Automating clinical note generation to reduce physician documentation time
• Improving AI-generated medical notes using structured clinical codes and ontologies
• Enhancing note quality through chain-of-thought prompting with domain knowledge

Innovation

• Combines Chain-of-Thought prompting with semantic search
• Infuses clinical ontology knowledge graphs for enrichment
• Uses ICD codes and patient data as input