Leveraging Large Language Models to Automate Patient-Clinical Trial Matching: An End-to-End Evaluation on Real-World Electronic Health Records

Core Concepts

Large language models can effectively match patients to appropriate clinical trials by directly processing unstructured electronic health records and trial eligibility criteria, outperforming existing approaches and nearly matching the performance of qualified medical experts.

Abstract

This study presents an end-to-end pipeline called PRISM (Patient Records Interpretation for Semantic Clinical Trial Matching) that leverages large language models (LLMs) to automate the process of matching patients to suitable clinical trials. The key highlights are: Scalable End-to-End Pipeline: PRISM directly ingests unstructured patient notes and clinical trial eligibility criteria to match patients to trials, without relying on rule-based processing. Fine-tuned LLM: The custom-tuned OncoLLM model outperforms GPT-3.5 and matches the performance of GPT-4, while being significantly smaller in size for deployment in privacy-sensitive healthcare settings. Benchmarking Against Medical Experts: The study demonstrates that LLMs can almost match the performance of qualified medical doctors in the task of clinical trial matching, highlighting their potential for real-world clinical applications. Comprehensive Evaluation: The pipeline is extensively evaluated on real-world electronic health records and clinical trials, going beyond constrained, synthetic datasets used in prior studies. Ranking Algorithm: A novel ranking algorithm is proposed that significantly improves the average position of relevant trials compared to baseline approaches. Bidirectional Search: The pipeline can be used for both patient-centric (identifying trials for a patient) and trial-centric (finding eligible patients for a trial) search directionalities. Overall, the study showcases the capabilities of LLMs in automating the complex task of patient-trial matching, with the potential to enhance clinical trial recruitment and improve patient access to therapeutic options.

Stats

Only about 7% of adults participate in cancer clinical trials. Key parameters for clinical trial screening are often absent in structured electronic health records, making the matching process challenging. The cost of running GPT-4 for patient-trial matching is approximately 35 times higher than using the custom-tuned OncoLLM model.

Quotes

"Our custom-tuned model, OncoLLM, demonstrates superior performance over GPT-3.5 and comparable efficacy to GPT-4. OncoLLM is significantly smaller than both and can be hosted within private infrastructure to address privacy concerns." "For the first time, we present evidence that LLMs can almost match the performance of qualified medical doctors for the task of clinical trial matching. This finding suggests the potential of LLMs for real-world clinical applications."

Key Insights Distilled From

PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models

by Shashi Kant ... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15549.pdf

Deeper Inquiries

How can the proposed pipeline be extended to incorporate structured data from electronic health records, in addition to unstructured notes, to further improve the accuracy of patient-trial matching?

Incorporating structured data from electronic health records (EHRs) alongside unstructured notes can significantly enhance the accuracy of patient-trial matching in the proposed pipeline. Here are some ways to extend the pipeline: Data Integration: Develop a data integration module that can extract structured data elements such as lab results, imaging reports, and demographic information from EHR systems. This module should harmonize the structured data with the unstructured notes to create a comprehensive patient profile. Feature Engineering: Utilize the structured data to engineer new features that capture important clinical indicators, disease progression markers, and treatment history. These features can provide additional context for the large language models (LLMs) to make more informed decisions. Semantic Interoperability: Ensure semantic interoperability between structured and unstructured data by mapping standardized medical ontologies and terminologies to facilitate seamless data processing and interpretation by the LLMs. Hybrid Retrieval: Implement a hybrid retrieval mechanism that combines embedding-based retrievers for unstructured data with structured query-based retrieval for structured data. This approach can ensure that all relevant information is considered during the matching process. Validation and Calibration: Validate the accuracy of the structured data extraction process and calibrate the pipeline to handle discrepancies between structured and unstructured data sources effectively. Regular validation checks can help maintain data integrity. Feedback Loop: Establish a feedback loop mechanism where the system learns from discrepancies between structured and unstructured data interpretations. This continuous learning process can improve the accuracy and reliability of the matching recommendations over time. By integrating structured data into the pipeline, the system can leverage a more comprehensive patient profile, leading to more precise patient-trial matching outcomes and enhancing the overall efficacy of the clinical decision support system.

How can the proposed pipeline be extended to incorporate structured data from electronic health records, in addition to unstructured notes, to further improve the accuracy of patient-trial matching?

Incorporating structured data from electronic health records (EHRs) alongside unstructured notes can significantly enhance the accuracy of patient-trial matching in the proposed pipeline. Here are some ways to extend the pipeline: Data Integration: Develop a data integration module that can extract structured data elements such as lab results, imaging reports, and demographic information from EHR systems. This module should harmonize the structured data with the unstructured notes to create a comprehensive patient profile. Feature Engineering: Utilize the structured data to engineer new features that capture important clinical indicators, disease progression markers, and treatment history. These features can provide additional context for the large language models (LLMs) to make more informed decisions. Semantic Interoperability: Ensure semantic interoperability between structured and unstructured data by mapping standardized medical ontologies and terminologies to facilitate seamless data processing and interpretation by the LLMs. Hybrid Retrieval: Implement a hybrid retrieval mechanism that combines embedding-based retrievers for unstructured data with structured query-based retrieval for structured data. This approach can ensure that all relevant information is considered during the matching process. Validation and Calibration: Validate the accuracy of the structured data extraction process and calibrate the pipeline to handle discrepancies between structured and unstructured data sources effectively. Regular validation checks can help maintain data integrity. Feedback Loop: Establish a feedback loop mechanism where the system learns from discrepancies between structured and unstructured data interpretations. This continuous learning process can improve the accuracy and reliability of the matching recommendations over time. By integrating structured data into the pipeline, the system can leverage a more comprehensive patient profile, leading to more precise patient-trial matching outcomes and enhancing the overall efficacy of the clinical decision support system.

How can the proposed pipeline be extended to incorporate structured data from electronic health records, in addition to unstructured notes, to further improve the accuracy of patient-trial matching?

Incorporating structured data from electronic health records (EHRs) alongside unstructured notes can significantly enhance the accuracy of patient-trial matching in the proposed pipeline. Here are some ways to extend the pipeline: Data Integration: Develop a data integration module that can extract structured data elements such as lab results, imaging reports, and demographic information from EHR systems. This module should harmonize the structured data with the unstructured notes to create a comprehensive patient profile. Feature Engineering: Utilize the structured data to engineer new features that capture important clinical indicators, disease progression markers, and treatment history. These features can provide additional context for the large language models (LLMs) to make more informed decisions. Semantic Interoperability: Ensure semantic interoperability between structured and unstructured data by mapping standardized medical ontologies and terminologies to facilitate seamless data processing and interpretation by the LLMs. Hybrid Retrieval: Implement a hybrid retrieval mechanism that combines embedding-based retrievers for unstructured data with structured query-based retrieval for structured data. This approach can ensure that all relevant information is considered during the matching process. Validation and Calibration: Validate the accuracy of the structured data extraction process and calibrate the pipeline to handle discrepancies between structured and unstructured data sources effectively. Regular validation checks can help maintain data integrity. Feedback Loop: Establish a feedback loop mechanism where the system learns from discrepancies between structured and unstructured data interpretations. This continuous learning process can improve the accuracy and reliability of the matching recommendations over time. By integrating structured data into the pipeline, the system can leverage a more comprehensive patient profile, leading to more precise patient-trial matching outcomes and enhancing the overall efficacy of the clinical decision support system.

How can the proposed pipeline be extended to incorporate structured data from electronic health records, in addition to unstructured notes, to further improve the accuracy of patient-trial matching?

Incorporating structured data from electronic health records (EHRs) alongside unstructured notes can significantly enhance the accuracy of patient-trial matching in the proposed pipeline. Here are some ways to extend the pipeline: Data Integration: Develop a data integration module that can extract structured data elements such as lab results, imaging reports, and demographic information from EHR systems. This module should harmonize the structured data with the unstructured notes to create a comprehensive patient profile. Feature Engineering: Utilize the structured data to engineer new features that capture important clinical indicators, disease progression markers, and treatment history. These features can provide additional context for the large language models (LLMs) to make more informed decisions. Semantic Interoperability: Ensure semantic interoperability between structured and unstructured data by mapping standardized medical ontologies and terminologies to facilitate seamless data processing and interpretation by the LLMs. Hybrid Retrieval: Implement a hybrid retrieval mechanism that combines embedding-based retrievers for unstructured data with structured query-based retrieval for structured data. This approach can ensure that all relevant information is considered during the matching process. Validation and Calibration: Validate the accuracy of the structured data extraction process and calibrate the pipeline to handle discrepancies between structured and unstructured data sources effectively. Regular validation checks can help maintain data integrity. Feedback Loop: Establish a feedback loop mechanism where the system learns from discrepancies between structured and unstructured data interpretations. This continuous learning process can improve the accuracy and reliability of the matching recommendations over time. By integrating structured data into the pipeline, the system can leverage a more comprehensive patient profile, leading to more precise patient-trial matching outcomes and enhancing the overall efficacy of the clinical decision support system.

What are the potential ethical and regulatory considerations in deploying large language models for clinical decision support systems, and how can these be addressed?

The deployment of large language models (LLMs) for clinical decision support systems raises several ethical and regulatory considerations that must be carefully addressed to ensure patient safety, data privacy, and ethical use of AI in healthcare. Here are some key considerations and strategies to mitigate associated risks: Data Privacy and Security: LLMs require access to sensitive patient data, making data privacy a paramount concern. Implement robust data encryption, access controls, and anonymization techniques to protect patient information. Adhere to data protection regulations such as HIPAA and GDPR to safeguard patient privacy. Bias and Fairness: LLMs can inadvertently perpetuate biases present in the training data, leading to unfair treatment of certain patient groups. Conduct bias assessments, diversify training data, and implement bias mitigation techniques to ensure fair and equitable outcomes for all patients. Transparency and Explainability: LLMs operate as black boxes, making it challenging to understand their decision-making processes. Enhance model transparency by providing explanations for recommendations and ensuring that clinicians can interpret and validate the system's outputs. Clinical Validation and Oversight: Before deployment, thoroughly validate the LLMs' performance in real-world clinical settings. Involve healthcare professionals in the development and validation process to ensure clinical relevance and accuracy of the system's recommendations. Regulatory Compliance: Comply with healthcare regulations and standards such as FDA guidelines for AI in healthcare. Ensure that the LLMs meet regulatory requirements for clinical decision support systems and undergo rigorous testing and validation before clinical use. Informed Consent and Patient Autonomy: Prioritize patient autonomy and informed consent when using LLMs for clinical decision support. Educate patients about the use of AI in their care, obtain consent for AI-driven recommendations, and provide patients with the option to opt-out of AI-based decision-making. Continual Monitoring and Evaluation: Establish mechanisms for ongoing monitoring, evaluation, and auditing of the LLMs' performance in clinical settings. Regularly assess the system's outcomes, address any issues or biases that arise, and update the model as needed to maintain accuracy and fairness. By proactively addressing these ethical and regulatory considerations, healthcare organizations can deploy LLMs for clinical decision support systems responsibly, ensuring patient safety, privacy, and trust in AI-driven healthcare technologies.

What are the potential ethical and regulatory considerations in deploying large language models for clinical decision support systems, and how can these be addressed?

The deployment of large language models (LLMs) for clinical decision support systems raises several ethical and regulatory considerations that must be carefully addressed to ensure patient safety, data privacy, and ethical use of AI in healthcare. Here are some key considerations and strategies to mitigate associated risks: Data Privacy and Security: LLMs require access to sensitive patient data, making data privacy a paramount concern. Implement robust data encryption, access controls, and anonymization techniques to protect patient information. Adhere to data protection regulations such as HIPAA and GDPR to safeguard patient privacy. Bias and Fairness: LLMs can inadvertently perpetuate biases present in the training data, leading to unfair treatment of certain patient groups. Conduct bias assessments, diversify training data, and implement bias mitigation techniques to ensure fair and equitable outcomes for all patients. Transparency and Explainability: LLMs operate as black boxes, making it challenging to understand their decision-making processes. Enhance model transparency by providing explanations for recommendations and ensuring that clinicians can interpret and validate the system's outputs. Clinical Validation and Oversight: Before deployment, thoroughly validate the LLMs' performance in real-world clinical settings. Involve healthcare professionals in the development and validation process to ensure clinical relevance and accuracy of the system's recommendations. Regulatory Compliance: Comply with healthcare regulations and standards such as FDA guidelines for AI in healthcare. Ensure that the LLMs meet regulatory requirements for clinical decision support systems and undergo rigorous testing and validation before clinical use. Informed Consent and Patient Autonomy: Prioritize patient autonomy and informed consent when using LLMs for clinical decision support. Educate patients about the use of AI in their care, obtain consent for AI-driven recommendations, and provide patients with the option to opt-out of AI-based decision-making. **Continual Monitoring and Evaluation