Assessment of a zero-shot large language model in measuring documented goals-of-care discussions.

ABSTRACT:

Context

Goals-of-care (GOC) discussions and their documentation are important process measures in palliative care. However, existing natural language processing (NLP) models for identifying such documentation require costly task-specific training data. Large language models (LLMs) hold promise for measuring such constructs with fewer or no task-specific training data.

Objective

To evaluate the performance of a publicly available LLM with no task-specific training data (zero-shot prompting) for identifying documented GOC discussions.

Methods

We compared performance of two NLP models in identifying documented GOC discussions: Llama 3.3 using zero-shot prompting; and, a task-specific BERT (Bidirectional Encoder Representations from Transformers)-based model trained on 4,

SUBMITTER: Lee RY 

PROVIDER: S-EPMC12140542 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature