  In psychiatry, negative patient descriptions and stigmatizing language can
contribute to healthcare disparities in two ways: (1) read by patients they can
harm their trust and engagement with the medical center; (2) read by future
providers they may negatively influence the future perspective of a patient. By
leveraging large language models, this work aims to identify the sentiment
expressed in psychiatric clinical notes based on the reader's point of view.
Extracting sentences from the Mount Sinai Health System's large and diverse
clinical notes, we used prompts and in-context learning to adapt three large
language models (GPT-3.5, Llama 2, Mistral) to classify the sentiment conveyed
by the sentences according to the provider or non-provider point of view.
Results showed that GPT-3.5 aligns best to provider point of view, whereas
Mistral aligns best to non-provider point of view.
