Enterprise AI Analysis
Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning
The extraction of critical patient information from Electronic Health Records (EHRs) poses significant challenges due to the complexity and unstructured nature of the data. Traditional machine learning approaches often fail to capture pertinent details efficiently, making it difficult for clinicians to utilize these tools effectively in patient care. This paper introduces a novel approach to extracting the OPQRST assessment from EHRs by leveraging the capabilities of Large Language Models (LLMs). We propose to reframe the task from sequence labeling to text generation, enabling the models to provide reasoning steps that mimic a physician's cognitive processes. This approach enhances interpretability and adapts to the limited availability of labeled data in healthcare settings. Furthermore, we address the challenge of evaluating the accuracy of machine-generated text in clinical contexts by proposing a modification to traditional Named Entity Recognition (NER) metrics. This includes the integration of semantic similarity measures, such as the BERT Score, to assess the alignment between generated text and the clinical intent of the original records. Our contributions demonstrate a significant advancement in the use of AI in healthcare, offering a scalable solution that improves the accuracy and usability of information extraction from EHRs, thereby aiding clinicians in making more informed decisions and enhancing patient care outcomes.
Authors: Zhimeng Luo (ZHL123E@PITT.EDU), Abhibha Gupta (ABG96@PITT.EDU), Adam Frisch (FRISCHAN@UPMC.EDU), Daqing He (DAH44@PITT.EDU)
Executive Impact
Leveraging Large Language Models for clinical data extraction yields significant improvements in efficiency and accuracy, directly impacting patient care and operational costs.
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F1-Score (Onset)
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F1-Score Increment (Self-Verification)
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F1-Score Increment (Reasoning Steps)
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Kappa Score (High Agreement)
Deep Analysis & Enterprise Applications
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Reframing EHR Information Extraction
The study transitions the task of extracting critical patient information (OPQRST) from traditional sequence labeling to text generation using Large Language Models (LLMs). This fundamental shift allows models to provide reasoning steps, mirroring a physician's cognitive process, thereby enhancing interpretability and usability in clinical settings.
| Feature | Traditional NER | LLM-based Generative Approach |
|---|---|---|
| Task Formulation | Sequence Labeling | Text Generation |
| Interpretability | Low (Black Box) | High (Reasoning Steps) |
| Data Dependency | High (Extensive Labeled Data) | Low (Few-Shot Learning) |
| Output Flexibility | Exact Matches Only | Semantically Similar Output (not just exact match) |
| Adaptability | Less adaptable to new contexts | Highly adaptable to diverse clinical narratives |
Mimicking Physician Thought Process with Prompt Engineering
A novel prompt engineering strategy was developed to guide LLMs to extract OPQRST entities by mimicking a physician's cognitive workflow. This involves multi-part prompts, including task definition, entity definitions, placeholder phrases for extraction, explicit reasoning steps, and a self-verification mechanism to reduce hallucinations.
Enterprise Process Flow
Task Definition & Guidelines
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Chief Complaint & OPQRST Entity Definitions
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Placeholder Phrase Identification (mimic physician scan)
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Explicit Reasoning Steps (template-guided)
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Self-Verification Step (reduce hallucinations)
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Few-Shot Examples (diverse scenarios)
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Final Output Tokenization (@ extraction)
Enhanced Evaluation with Semantic Similarity
To overcome limitations of traditional Named Entity Recognition (NER) metrics that only capture exact matches, the study proposes an adaptation integrating semantic similarity measures (e.g., BERTScore, PromptLLM). This new metric provides a more comprehensive assessment of generated text, recognizing contextual accuracy crucial for clinical application.
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F1-Score for Onset (Our Method)
Our proposed method achieved a strong F1-Score of 0.944 for 'Onset' extraction, demonstrating its high accuracy in identifying key temporal markers for chief complaints.
Comparative Performance of Prompting Methods
The study evaluates various prompting techniques for OPQRST extraction, demonstrating that the proposed reasoning-based method significantly outperforms baseline approaches like Prefix, Cloze, Anticipatory, CoT, and Heuristic prompting, particularly for entities requiring complex contextual understanding.
| Entity | Prefix | Cloze | Anticipatory | COT | Heuristic | Our method |
|---|---|---|---|---|---|---|
| Onset | 0.424 | 0.578 | 0.618 | 0.488 | 0.303 | 0.944 |
| Provocation | 0.18 | 0.247 | 0.16 | 0.762 | 0.752 | 0.836 |
| Palliation | 0.182 | 0.143 | 0.47 | 0.695 | 0.85 | 0.941 |
| Quality | 0.235 | 0.400 | 0.351 | 0.163 | 0.603 | 0.673 |
| Region | 0.464 | 0.302 | 0.351 | 0.346 | 0.667 | 0.736 |
| Radiation | 0.295 | 0.517 | 0.152 | 0.409 | 0.68 | 0.889 |
| Severity | 0.158 | 0.179 | 0.082 | 0.079 | 0.478 | 0.838 |
Advancing AI for Clinical Information Extraction
This approach represents a significant advancement in healthcare AI by offering a scalable, interpretable solution for extracting complex clinical information from EHRs. It addresses the challenges of limited labeled data and the need for explainable AI in high-stakes medical contexts, paving the way for more informed clinical decisions and enhanced patient care.
Scalable & Interpretable AI for Healthcare
Our LLM-based method provides a robust framework for extracting crucial patient information like OPQRST from unstructured EHRs. By simulating physician's reasoning, it not only improves accuracy but also offers unprecedented transparency, which is vital for clinical adoption. This innovation reduces reliance on extensive labeled datasets, making AI solutions more accessible and scalable for diverse healthcare settings. The ability to understand why an AI makes a certain extraction builds clinician trust and facilitates integration into real-world workflows, ultimately enhancing patient care outcomes and operational efficiency.
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