Why LLM reasoning isn't enough for medical scheduling math
I’ve seen plenty of people try to make Claude or GPT-4 act like a specialized scheduler. They prompt it heavily: "You are a precise medical assistant. Calculate the next five doses based on X frequency." The model usually gets it right once. Then it fails when you add constraints—like shifting the entire schedule to land on a Thursday instead of a Tuesday—or when you ask it to validate whether a…
The article discusses the limitations of using Large Language Models (LLMs) like Claude or GPT-4 for specialized tasks such as medical scheduling. While these models can generate correct results for simple scheduling problems, they often fail when faced with additional constraints or validation tasks. This is because LLMs are probabilistic engines, whereas scheduling is a deterministic problem.
When the two worlds collide without proper interfaces, it leads to "hallucinations" that are not just annoying but dangerous in healthcare settings. The author argues that instead of relying on LLMs to perform complex calculations, it's better to give them tools that handle the heavy lifting via structured execution. The Injection Day Alignment MCP server is introduced as a solution for medication management, providing specific primitives like get_injection_schedule, calculate_shift_offset, and verify_schedule_compliance to handle schedule adjustments, offset calculations, and compliance checks respectively.
The author emphasizes that these tools are essential for building reliable AI agents in sensitive domains like medicine and finance, rather than just scaling technical debt with custom code.
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