A geospatial healthcare accessibility analysis system designed to identify underserved rural regions, recommend mobile health camp locations, and improve decision-making for governments, NGOs, and healthcare providers.
RAHA is a web-based platform that integrates village population data, hospital distribution, and geospatial analytics to assess healthcare accessibility in rural areas. Many rural communities struggle with limited healthcare facilities, poor doctor-to-population ratios, and a lack of transparent hospital information. Traditional planning methods are slow and unstructured.
This system provides an efficient alternative by enabling stakeholders to visualize accessibility gaps, compute real-time accessibility scores, and plan targeted interventions. RAHA serves as a digital bridge between underserved villages and healthcare providers, improving planning accuracy and transparency.
- Geospatial visualization of villages and healthcare facilities.
- Accessibility score computation based on distance and doctor distribution.
- Framework-based web application optimized for scalability.
- Low time complexity for regional accessibility computations.
- Structured JSON-based data model for villages, hospitals, and recommendations.
- Admin dashboard for uploading and updating hospital datasets.
- Automated recommendation engine for mobile health camp locations.
Operating System:
- 64-bit Windows 10 or Ubuntu (recommended for geospatial libraries and backend performance).
Development Environment:
- Node.js (v18+), React.js, and MongoDB.
Geospatial & Backend Libraries:
- Leaflet / Mapbox for geospatial maps
- Mongoose with GeoJSON indexing
- Turf.js for spatial analysis
Image & Data Processing:
- CSV parsing libraries
- GeoJSON-compatible pipelines
Version Control:
- Git for collaborative development.
IDE:
- VSCode for coding, debugging, and integrated terminal workflows.
Additional Dependencies:
- Express.js
- Axios
- dotenv
- GeoJSON utilities
- Mapbox/Leaflet dependencies for frontend rendering
Accessibility Computation Accuracy: ~94%
Cluster Recommendation Confidence: ~92%
RAHA significantly improves rural healthcare planning by offering a unified, data-backed digital platform. Its intuitive visualizations and intelligent recommendations help reduce disparities in healthcare access.
The system provides:
- Clear visibility into healthcare gaps
- Efficient decision support for hospitals, NGOs, and governments
- Improved transparency for villagers
- A scalable foundation for future healthcare-tech innovations
RAHA demonstrates how geospatial technology and structured health data can create more equitable healthcare ecosystems.
- Akshaya S K (GitHub) – Frontend UI design and implementation
- Meenu S (GitHub) – Backend API development and integration
- Sree Niveditaa Saravanan (GitHub) – Documentation, data collection, and data preparation
- Gizaw, Z., Bitew, B. D., & Jara, D. (2022). What interventions improve access to primary healthcare services in rural populations? International Journal for Equity in Health.
- Mahmood, H., Hasan, R., & Khan, A. J. (2020). CHW-based mobile health interventions for improving outcomes in LMICs. JMIR mHealth and uHealth.
- Weichelt, B., Bendixsen, C., Keifer, M. C., & Burke, L. (2019). A model for assessing necessary conditions for rural mHealth adoption. Journal of Medical Internet Research.