Clinical requirements and clinical prompts are not interchangeable. Yet, in health tech, we routinely separate them—and risk losing clinical nuance, wasting time, and compromising accuracy in the process.
We ask clinical experts to write the “requirements” and technical analysts to write the “prompts.” On paper, this division of labor makes sense. In practice, it can create a frustrating, recurring bottleneck.
Here is how the cycle unfolds:
• The Flaw: A clinician identifies a subtle clinical error in an AI model’s output.
• The Blueprint: They document the clinical nuance in a requirements file.
• The Translation: An analyst interprets those requirements and translates them into prompt logic.
• The Break: The revised prompt introduces a new clinical error or fails to capture the original intent.
The cycle repeats.
The problem isn’t necessarily the analyst’s skill or the clinician’s expertise. It’s the workflow.
LLM outputs are sensitive to wording, context, and prompt structure. Clinical reasoning is dynamic—not a static software specification. When we force a linear handoff between clinical intent and technical execution, we risk losing the very nuance we’re trying to preserve.
If we want safer, more accurate clinical AI, we must bring clinical reasoning directly into prompt development.
Three practical changes can help:
1. Turn requirements into test cases. Don’t just list rules. Provide concrete clinical scenarios: a typical case, a negative case, and challenging edge cases. Define the expected clinical interpretation and explain why.
2. Define the logic, not just the output. Include examples of the clinical logic the model should follow—and the pitfalls it must avoid.
3. Pair clinicians and analysts during prompt development. A focused, live working session can surface misunderstandings immediately, rather than letting them multiply through rounds of asynchronous revisions.
Clinical expertise cannot be bookended—confined to defining the initial intent and validating the final output while implementation happens in between.
The goal isn’t for clinicians to replace analysts or vice versa. It’s to ensure neither has to guess what the other means.