  Medical errors in clinical text pose significant risks to patient safety. The
MEDIQA-CORR 2024 shared task focuses on detecting and correcting these errors
across three subtasks: identifying the presence of an error, extracting the
erroneous sentence, and generating a corrected sentence. In this paper, we
present our approach that achieved top performance in all three subtasks. For
the MS dataset, which contains subtle errors, we developed a retrieval-based
system leveraging external medical question-answering datasets. For the UW
dataset, reflecting more realistic clinical notes, we created a pipeline of
modules to detect, localize, and correct errors. Both approaches utilized the
DSPy framework for optimizing prompts and few-shot examples in large language
model (LLM) based programs. Our results demonstrate the effectiveness of LLM
based programs for medical error correction. However, our approach has
limitations in addressing the full diversity of potential errors in medical
documentation. We discuss the implications of our work and highlight future
research directions to advance the robustness and applicability of medical
error detection and correction systems.
