Kee Yuan Ngiam

Prompt Engineering for Eastern Cooperative Oncology Group Status Extraction: Comparing Large Language Model Techniques

PURPOSE Eastern Cooperative Oncology Group (ECOG) performance status is critical for cancer patient management, yet it is often documented only in unstructured clinical notes. This study compares several approaches to extract ECOG status from oncology notes, focusing on advanced prompting techniques for large language models (LLMs). METHODS We evaluated four ECOG extraction approaches on unstructured clinical notes from patients with non–small cell lung cancer, multiple myeloma, or ovarian cancer (2017-2021). The approaches were a rule-based natural language processing algorithm, simple LLM prompting, and two advanced prompts (chain-of-thought and Double Filtering) using a domain-tuned LLM (LLAMAv3.2). Performance was measured on a binary outcome (any ECOG documented v none) and a three-class outcome (ECOG 0-1 v ≥2 v none) and via an adapted QUEST questionnaire for human evaluation. RESULTS Both CoT and double filtering technique (DFT) achieved 94% accuracy, outperforming the rule-based method (91%) and simple prompting (86%). DFT had the highest specificity (0.91) and positive predictive value (PPV; 0.93), whereas CoT attained the highest sensitivity (0.98). In the QUEST evaluation, DFT and CoT scored higher on output quality, reasoning, bias reduction, and user satisfaction than the simple prompt. DFT received the top satisfaction rating. In the three-class analysis, DFT and CoT again performed best (accuracy 0.91 v 0.87) and DFT was most sensitive for ECOG ≥2 cases. Estimates for ECOG ≥2 remained imprecise because of the small sample (n = 20). All methods sometimes hallucinated ECOG status. CONCLUSION Advanced LLM prompting improved ECOG extraction over basic methods. DFT and CoT each showed specific strengths (DFT had higher PPV and user satisfaction; CoT achieved higher sensitivity). These approaches appear to be generalizable across cancer types. Key implementation considerations include computational cost and human oversight. Overall, advanced prompting can standardize ECOG documentation, accelerate patient cohort identification, and inform personalized treatment planning.