data-structurer

Transform multi-source medical reports into schema-validated JSON for visualization.

Updated Aug 27, 2026

Search Tags:#timeline #visualization #data-extraction #clinical-dashboards #json-structure #medical-analysis

Ready to run

npx skills add https://github.com/n1healthcare/easy-chr --skill data-structurer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-structurer
Source: https://github.com/n1healthcare/easy-chr/tree/main/server/.gemini/skills/data-structurer
Command: npx skills add https://github.com/n1healthcare/easy-chr --skill data-structurer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill translates complex medical analyses into a single, structured JSON object designed for immediate visualization in dashboards and charts.

Core Features & Use Cases

Structured Data Output: Produces a chart-ready JSON with sections for meta, diagnoses, timeline, prognosis, supplements, lifestyle, and monitoring.
Deterministic Visualization Output: Ensures all numeric values, dates, and statuses follow a consistent schema for reliable visualization and automation.
Use Case: Given a multi-source medical report, generate a JSON payload that a frontend can render into charts, timelines, and dashboards.

Quick Start

Run the data-structurer to convert your medical report bundle into a single JSON payload.

Frequently Asked Questions about data-structurer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert multi-source clinical reports into chart-ready JSON for visualization?▼

Converting multi-source clinical reports into chart-ready JSON involves compiling diagnoses, timelines, prognosis, supplements, lifestyle, and monitoring data into a single validated JSON object. This structured payload matches a defined schema for deterministic frontend loading.

What is the best way to structure medical analysis data for clinical dashboards?▼

Structuring medical analysis data for clinical dashboards requires transforming multi-source reports into a JSON object with distinct sections for meta, diagnoses, timeline, prognosis, supplements, lifestyle, and monitoring. This ensures reliable visualization and automation without side effects.

How does deterministic JSON schema validation work for medical timeline visualization?▼

Deterministic JSON schema validation for medical timeline visualization works by enforcing consistent formatting for all numeric values, dates, and statuses. This schema compliance ensures reliable chart rendering and automation directly from the extracted clinical data.

Can I use structured JSON to map diagnoses and prognosis data from medical reports?▼

Yes, you can use structured JSON to map diagnoses and prognosis data by extracting these elements from multi-source medical reports. The process organizes clinical information into dedicated JSON sections designed for immediate dashboard integration.

Do I need a defined schema to generate chart-ready JSON from medical analyses?▼

Yes, you need a defined schema to generate chart-ready JSON from medical analyses. A validated schema ensures all compiled clinical data—diagnoses, timelines, monitoring—follows consistent numeric and date formatting for deterministic visualization loading.

When do I need to compile supplements and lifestyle data into a single JSON payload?▼

You need to compile supplements and lifestyle data into a single JSON payload when preparing multi-source medical reports for frontend visualization. Consolidating this data enables dashboards to render comprehensive clinical timelines and monitoring charts accurately.