E dinburgh Clinical NLP at MEDIQA - CORR 2024: Guiding Large Language Models with Hints (2024)

Abstract: The MEDIQA-CORR 2024 shared task aims to assess the ability of Large Language Models (LLMs) to identify and correct medical errors in clinical notes. In this study, we evaluate the capability of general LLMs, specifically GPT-3.5 and GPT-4, to identify and correct medical errors with multiple prompting strategies. Recognising the limitation of LLMs in generating accurate corrections only via prompting strategies, we propose incorporating error-span predictions from a smaller, fine-tuned model in two ways: 1) by presenting it as a hint in the prompt and 2) by framing it as multiple-choice questions from which the LLM can choose the best correction. We found that our proposed prompting strategies significantly improve the LLM’s ability to generate corrections. Our best-performing solution with 8-shot + CoT + hints ranked sixth in the shared task leaderboard. Additionally, our comprehensive analyses show the impact of the location of the error sentence, the prompted role, and the position of the multiple-choice option on the accuracy of the LLM. This prompts further questions about the readiness of LLM to be implemented in real-world clinical settings.

## introduction medical errors represent a major concern in the healthcare sector, leading to adverse patient outcomes and higher costs for healthcare providers. the detection and correction of such medical errors are critical in enhancing healthcare delivery and outcomes. recognising the importance of efficient and precise medical documentation, the mediqa-corr 2024 shared task @xcite is initiated to evaluate the potential of using large language models (llms) as solutions to locate and correct medical errors within clinical notes. in our study, we evaluated multiple prompting strategies such as in-context learning (icl) and chain-of-thought (cot) to enhance the performance of llms, specifically focusing on gpt-3.5 and @xcite . we proposed incorporating a smaller fine-tuned language model, namely biolinkbert @xcite , to aid llms in locating an error span in a clinical note. we incorporated the predicted error span in two ways: 1) by presenting it as a hint in the prompt to direct the error correction, and 2) by framing it as multiplechoice questions where the llm can select the most probable correction. our findings revealed that the llms show noticeable improvements in their generation capability when presented with more icl examples. similarly, the cot prompt also improves the error correction capability of the llms. among the different reasoning styles we experimented with, the llm performs the best with brief reasoning. our prompt design, which provides a hint about the typical nature of the errors and a hint from the error span prediction, further improves the llms' ability to generate corrections. the combination of 8-shot icl with brief cot reasoning and hints is the best-performing prompting strategy in the two provided validation sets. this pipeline ranked sixth in the shared task leaderboard. in summary, our contributions are as follows: ## system overview we experimented with three strategies: end-to-end prompting strategy for error correction: this strategy treats all three subtasks as a single prompting task. the llm simultaneously predicts if the clinical note contains an error, pinpointing its location, and proposing a correction. ## results our experiments are structured as answers to sequential research questions. firstly, we conducted experiments to find the best model for error span prediction, evaluating them on em and f1 scores. subsequently, we experimented with various prompting strategies for error correction, evaluating them on the macro-averaged accuracy and aggregate nlg scores across ms and uw datasets. the first error correction experiment starts with an end-to-end prompting approach, relying solely on the llm capability with icl and cot to correct errors. we, then, experimented with integrating the error span prediction model into the error correction process via the mcq-style prompt. lastly, we experimented with the hybrid approach, integrating the error span prediction as a hint for the end-to-end prompting approach. we used gpt-3.5 in our error correction experiments on the validation sets foot_1 , choosing the best prompting strategy to be implemented with gpt-4 on the test set. rq1: how well are the smaller lms performing in the error span detection? as shown in among all models, biolinkbert-large showed the highest em and f1 scores on the ms validation set, indicating a superior ability to predict error spans within clinical notes. this suggests that the domain-adaptive pretraining that biolinkbert has undergone contributes to its performance in medical error detection tasks. however, all models struggle to accurately predict error spans on the uw validation set. recognising this, we trained biolinkbert-large on the ms train dataset and 25% of the uw validation dataset as the error span prediction model for the subsequent experiments. rq2: can llms perform well end-to-end solely with prompting strategies? before leveraging the error span prediction, we began our error correction experiment by solely relying on the llm with prompting strategies to correct errors without any help from the error span prediction. this prompt-only end-to-end approach serves as the baseline for our proposed solutions. ## post-hoc analyses commonly reported nlg metrics tend to not be well correlated with human judgement, especially in the clinical domain @xcite . to understand the limitations of llms for clinical note correction, we extend beyond the reported performance metrics by analysing the sensitivity of llms to the data and prompt, as well as the common mistakes that llms tend to commit. foot_2 ## conclusion this study explores strategies for using llms to detect and correct medical error for the mediqa-corr 2024 shared task. in addition to the comprehensive evaluation of prompting strategies based on different reasoning styles, we experiment with integrating error-span predictions from a fine-tuned model. our best-performing system includes a fine-tuned biolinkbert-large for error-span prediction and gpt-4 for error correction. by harnessing llms' generative abilities with 8-shot icl and brief cot and presenting predicted error span as a hint in the prompt, our best-performing solu-tion ranked sixth in the shared task leaderboard. our post-hoc analyses offer insights into the use of llm in medical error correction, including sensitivity to error location, role-playing bias, and common types of mistakes made by llms.