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.