  This paper describes our submission to the MEDIQA-CORR 2024 shared task for
automatically detecting and correcting medical errors in clinical notes. We
report results for three methods of few-shot In-Context Learning (ICL)
augmented with Chain-of-Thought (CoT) and reason prompts using a large language
model (LLM). In the first method, we manually analyse a subset of train and
validation dataset to infer three CoT prompts by examining error types in the
clinical notes. In the second method, we utilise the training dataset to prompt
the LLM to deduce reasons about their correctness or incorrectness. The
constructed CoTs and reasons are then augmented with ICL examples to solve the
tasks of error detection, span identification, and error correction. Finally,
we combine the two methods using a rule-based ensemble method. Across the three
sub-tasks, our ensemble method achieves a ranking of 3rd for both sub-task 1
and 2, while securing 7th place in sub-task 3 among all submissions.
