Structured prompting as reusable clinical tools.

Objectives: Large language models (LLMs) are increasingly used by clinicians, yet most clinical interactions remain disposable: a question asked, an answer received, and the reasoning discarded. Just as the stethoscope required physicians to learn a skill before it became clinically useful, LLMs demand a learned technique, prompting, to unlock their potential. We present a practical framework for progressing from passive artificial intelligence (AI) use to reusable, structured clinical reasoning prompts. Methods: We propose a 5-level hierarchy of clinician-to-AI interaction, progressing from search (level 1), through conversational prompting (level 2), persistent prompting (level 3), and document-grounded project workspaces (level 4), to structured clinical reasoning prompts, termed skills, versioned, auditable, and iteratively improvable (level 5). The framework is illustrated with a proof-of-concept skill: a multidisciplinary diagnostic deliberation prompt for complex clinical cases, deployed as an open-access web application. Results: Each level is intended to add structure, reproducibility, and clinical value, though this has not been formally evaluated. Skills prompt clinical reasoning into reusable protocols with defined inputs, logic gates, verification safeguards, and tiered outputs. The case example demonstrates that clinicians can author such prompts in natural language without software engineering expertise, applying principles of version control and failure-driven iteration familiar from clinical research. Conclusions: Prompting is an acquired clinical skill, not an innate ability. A shared framework and vocabulary for structured AI interaction can help clinicians, educators, and policymakers move from disposable AI use towards more structured, auditable approaches to AI-assisted reasoning that support, but do not replace, clinical judgement.