Fine-tuning Zero-shot Large Language Models for Patient-reported Outcomes (Student 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 4,642 manually annotated notes. We tested both models on records from a series of clinical trials enrolling adult patients with chronic life-limiting illness hospitalized over 2018-2023. We evaluated the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), and maximal F 1 score, for both note-level and patient-level classification over a 30day period. Results: In our text corpora, GOC documentation represented <1% of text and was found in 7.3-9.9% of notes for 23-37% of patients. In a 617-patient held-out test set, Llama 3.3 (zeroshot) and BERT (task-specific, trained) exhibited comparable performance in identifying GOC documentation (Llama 3.3: AUC 0.979, AUPRC 0.873, and F 1 0.83; BERT: AUC 0.981, AUPRC 0.874, and F 1 0.83). Conclusion: A zero-shot large language model with no task-specific training performed similarly to a task-specific trained BERT model in identifying documented goals-of-care discussions. This demonstrates the promise of LLMs in measuring novel clinical research outcomes.