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Retrieving Evidence from EHRs with Large Language Models: Possibilities and Challenges

Core Concepts
The author proposes using Large Language Models (LLMs) to efficiently retrieve and summarize unstructured evidence in patient Electronic Health Records (EHRs) to aid radiologists in diagnosis. The approach shows promise but also highlights challenges such as hallucinations.
The content discusses the use of LLMs to extract evidence from EHRs for diagnostic support, emphasizing the challenges and potential benefits of this approach. It introduces a novel strategy for evaluating the effectiveness of LLM-generated evidence through manual and automated methods, showcasing the advantages and limitations of each. The study provides insights into the practical application of LLMs in healthcare settings, highlighting the need for further research to optimize their performance. Unstructured data in EHRs contains critical information complementary to imaging. LLMs can efficiently retrieve and summarize evidence relevant to a given query. Expert evaluation shows LLM-based outputs outperform pre-LLM methods. Automated evaluation using LLMs correlates well with expert assessments. Challenges include hallucinations and weakly correlating evidence. Scaling up evaluation with LLM-based automation shows promising results.
Flan-T5 generated 188 instances for intracranial hemorrhage in MIMIC dataset. Mistral-Instruct produced 117 instances for small vessel disease in BWH dataset.
"LLMs provide an attractive mechanism to permit interactions with unstructured EHR data." "Our work shows the potential of LLMs as interfaces to EHRs."

Key Insights Distilled From

by Hiba Ahsan,D... at 03-05-2024
Retrieving Evidence from EHRs with LLMs

Deeper Inquiries

How can model confidence help mitigate hallucinated content in LLM-generated summaries?

Model confidence can play a crucial role in mitigating hallucinated content in LLM-generated summaries. By assessing the confidence level of the model's output, we can determine the reliability of the information provided. If a generated summary has high confidence, it is more likely to be accurate and relevant to the query diagnosis. On the other hand, if the model outputs low-confidence information, it may indicate that the content is speculative or potentially fabricated (hallucinated). To leverage model confidence effectively: Threshold Setting: Establishing a threshold for acceptable confidence levels can help filter out unreliable or hallucinated content. Flagging Mechanism: Implementing a system where low-confidence outputs are flagged for further review by human experts can ensure that only trustworthy information is presented. Confidence Calibration: Continuously calibrating and monitoring model confidence based on feedback and evaluation results can improve its accuracy over time. By using model confidence as a guiding factor, healthcare professionals can have more trust in LLM-generated summaries and rely on them with greater assurance when making diagnostic decisions.

What are the implications of weakly correlating evidence on clinical decision-making?

Weakly correlating evidence poses significant challenges to clinical decision-making as it introduces ambiguity and uncertainty into the diagnostic process. Some implications include: Diagnostic Accuracy: Weak correlations may lead to incorrect diagnoses or misinterpretations of patient conditions, impacting overall accuracy. Treatment Planning: Inaccurate or irrelevant evidence could influence treatment plans negatively, potentially resulting in suboptimal patient care. Resource Allocation: Healthcare providers may waste valuable time investigating weakly correlated evidence that does not contribute meaningfully to diagnosis or treatment decisions. Patient Outcomes: Incorrect diagnoses based on weak correlations could result in adverse outcomes for patients if appropriate interventions are not implemented promptly. Addressing weakly correlating evidence requires careful consideration during data interpretation and validation processes to ensure that only clinically relevant and reliable information influences decision-making.

How can automated evaluation using LLMs be further optimized for scalability and accuracy?

Automated evaluation using LLMs holds great potential for scalability but requires optimization strategies for enhanced accuracy: Fine-tuning Models: Fine-tune evaluator LLMs specifically for medical text analysis tasks to improve performance on healthcare-related evaluations. Data Augmentation: Increase training data diversity by augmenting datasets with synthetic examples or incorporating additional sources of medical text data. Prompt Refinement: Iteratively refine prompts used during evaluation based on feedback from expert annotations to enhance relevance detection capabilities. Ensemble Methods: Employ ensemble techniques by combining multiple evaluative models' outputs to reduce bias and variance while enhancing overall assessment quality. Continuous Monitoring: Regularly monitor performance metrics such as F1 scores, PCC values, and AUC curves to identify areas needing improvement over time. By implementing these optimization strategies systematically, automated evaluation using LLMs can achieve higher levels of scalability without compromising accuracy in assessing clinical evidence extracted from electronic health records (EHRs).