iHealth Edu
A digital health platform that brings structured screening, health education, patient records, IoT health data, and machine learning decision support into one system.
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Project Overview
iHealth Edu was developed with Puskesmas Padangsari to bring health records, structured screening, and educational content into a digital platform designed around primary-care workflows.
The system centralizes patient information, makes health education easier to access, and helps health workers review patient histories. Machine learning results are presented only as decision support and do not provide a clinical diagnosis.
Machine learning results are presented only as decision support for health workers and do not provide a clinical diagnosis or medical advice.
My Contribution
- Gathered requirements through an interview with the head of Puskesmas Padangsari, regular discussions, and workflow observation, then translated them into the UI/UX design.
- Developed role-specific frontend experiences for patients, administrators, and health workers using Next.js.
- Integrated REST APIs and presented health measurements received from ESP32 devices in the frontend.
- Integrated machine learning decision-support results into the health-worker interface.
- Figma
- Next.js
- React
- TypeScript
- Tailwind CSS
- REST API
System Scope
- Patient
Completes screening, accesses educational content, and views health history.
- Administrator
Manages accounts, content, and operational system data.
- Health Worker
Monitors patient data and reviews decision-support results.
Blood pressure, Blood glucose, Cholesterol
Height, Weight, Lifestyle information, Supporting patient information
Centralized patient history for appropriate role-based monitoring.
The architecture connects IoT data and machine learning outputs with the Next.js interface. My contribution focused on frontend development and integrating these data flows into the user interface.