Prompting is All You Need: How to Make LLMs More Helpful for Clinical Decision Support

IMPORTANCE: Large language models (LLMs) offer potential decision support, but their accuracy varies. Prompt engineering can generally enhance LLM behavior in a clinical context, yet best practices have yet to be formally explored in realistic clinical contexts for neurology. OBJECTIVE: To evaluate the impact of structured prompting versus naive prompting on the performance of four LLMs (two closed-source: OpenAI GPT-4o, OpenAI o3; three open-source: Meta Llama-4-Scout-17B-16E-Instruct, Llama-3.3-70B-Instruct-Turbo, and the reasoning model r1-1776) for thrombolytic clinical decision support (CDS) in acute stroke. DESIGN: Models responded to three novel ischemic stroke vignettes using either a naive question ("Should this patient be offered thrombolytics?") or a five-step structured prompt (CARDS) guiding information extraction, timing analysis, contraindication checking, decision process explanation, and risk-benefit discussion. Outputs were assessed across seven domains: guideline adherence, unsafe recommendations, risk recognition, guideline grading accuracy, inclusion of conversational explanation, clarity, and overall helpfulness. RESULTS: Structured prompts significantly enhanced performance across most domains, with varying effects between model families. For closed-source models (GPT-4o, o3), prompts structured in the CARDS style improved guideline adherence from 83.3% to 100%, eliminated unsafe recommendations (16.7% to 0%), and increased specific guideline grading accuracy from 0% to 100%. Similarly, the open-source reasoning model r1-1776 achieved these top-tier outcomes (100% adherence, 0% unsafe, 100% grading, 100% conversation) when structured prompts were applied, with grading and conversation improving from 0%. In contrast, other open-source models (Llama-4-Scout, Llama-3.3-70B) showed more modest gains: risk recognition improved (83.3% to 100%) and guideline grading accuracy increased (0% to 66.7%), while guideline adherence (66.7%) and unsafe recommendations (33.3%) persisted. Overall, structured prompting yielded the largest improvements in guideline grading accuracy and conversational reasoning across multiple models. CONCLUSION AND RELEVANCE: Structured prompting substantially enhances LLM performance for acute stroke thrombolysis CDS. Notably, some models, including the proprietary GPT-4o and o3, and the open-source reasoning model r1-1776, achieved excellent safety and adherence with structured prompts. For clinical deployment of any LLM, structured prompts are crucial, and vigilant human oversight remains essential.