Normalize drug, PICO, and clinical-question inputs into a structured data model.
npx skills add https://github.com/tito-gh/healthcare --skill input-normalizer
Please help me install this Agent Skill. Skill: input-normalizer Source: https://github.com/tito-gh/healthcare/tree/main/.claude/skills/input-normalizer Command: npx skills add https://github.com/tito-gh/healthcare --skill input-normalizer
The Input Normalizer unifies three distinct clinical input modes—drug-origin, PICO-origin, and clinical-question origin—into a consistent, structured data model to streamline downstream RWD study planning.
Provide a sample normalization for a given input set to demonstrate the end-to-end flow.
High-intent search queries and answers about installing and using this skill.
You can normalize heterogeneous clinical inputs by mapping drug, PICO, and clinical question origins into a single deterministic data model. This structured format ensures explicit validation steps and compatibility with downstream RWD study planning workflows.
Clinical input normalization unifies diverse formats like drug-origin and PICO data into a consistent schema. You need it for oncology and pharmacology workflows to generate standardized research questions and ensure deterministic study design elements.
Yes, the input normalizer infers and maps clinical question origins into PICO elements including populations, interventions, comparators, and outcomes. It generates standardized research questions to drive RWD planning and documentation.
Yes, the normalizer produces deterministic outputs with explicit mappings and validation steps specifically designed for compatibility with downstream RWD study design and planning Skill modules.
The best way to standardize pharmacology inputs is to unify drug, PICO, and clinical question formats into a single structured data model. This approach ensures explicit mappings and deterministic outputs for research question generation.
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