Home / Current Issue / Paper 1724097
CareBridge AI: A Unified Web-Based Healthcare Assistance Platform
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Digital Healthcare
Abstract
Healthcare-support information is often spread across separate services for treatment-cost planning, government assistance discovery, hospital location search, and medical-document review. This can make navigation difficult for patients and families, particularly those seeking affordable care. This paper presents CareBridge AI, a web-based prototype that organizes selected healthcare-support functions in one interface. Developed using HTML, CSS, and JavaScript, the prototype provides interfaces for medical-bill information, a funding-gap calculator, medical-report text review, scheme discovery, a hospital finder, and chat-style support. The funding-gap module estimates the remaining amount from user-entered treatment cost, available funds, expected assistance, and other contributions. The current implementation uses browser-side logic and demonstration storage. It does not demonstrate a trained or clinically validated AI/ML model, a production-grade patient database, or an IoT deployment. A qualitative comparison identifies integration and navigation as the main design opportunity. A proposed evaluation plan covers calculation correctness, official-link validity, usability, privacy, and future AI validation. No clinical or performance improvement is claimed without measured testing.
Keywords
digital health; healthcare assistance; healthcare costs; hospital finder; scheme discovery; web application
References
[1] World Health Organization. Global strategy on digital health 2020–2025. Geneva: World Health Organization; 2021. https://www.who.int/publications/i/item/9789240020924
[2] Vasanthan L, Natarajan SK, Babu A, Kamath MS, Kamalakannan S. Digital health interventions for improving access to primary care in India: A scoping review. PLOS Global Public Health. 2024;4(5):e0002645. https://doi.org/10.1371/journal.pgph.0002645
[3] Morid MA, Kawamoto K, Ault T, Dorius J, Abdelrahman S. Supervised learning methods for predicting healthcare costs: Systematic literature review and empirical evaluation. AMIA Annual Symposium Proceedings. 2018;2017:1312–1321.
[4] Government of India. myScheme: One-stop search and discovery platform for government schemes. https://www.myscheme.gov.in/
[5] Google. Google Maps Platform documentation. https://developers.google.com/maps/documentation
[6] WHATWG. HTML living standard. https://html.spec.whatwg.org/
[7] MDN Web Docs. Web technology references. https://developer.mozilla.org/en-US/docs/Web
How to cite this paper
@article{1724097,
author = {Saniya Javid Shaikh, Pawar A. N.},
title = {CareBridge AI: A Unified Web-Based Healthcare Assistance Platform},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {2650-2653},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724097.pdf},
abstract = {Healthcare-support information is often spread across separate services for treatment-cost planning, government assistance discovery, hospital location search, and medical-document review. This can make navigation difficult for patients and families, particularly those seeking affordable care. This paper presents CareBridge AI, a web-based prototype that organizes selected healthcare-support functions in one interface. Developed using HTML, CSS, and JavaScript, the prototype provides interfaces for medical-bill information, a funding-gap calculator, medical-report text review, scheme discovery, a hospital finder, and chat-style support. The funding-gap module estimates the remaining amount from user-entered treatment cost, available funds, expected assistance, and other contributions. The current implementation uses browser-side logic and demonstration storage. It does not demonstrate a trained or clinically validated AI/ML model, a production-grade patient database, or an IoT deployment. A qualitative comparison identifies integration and navigation as the main design opportunity. A proposed evaluation plan covers calculation correctness, official-link validity, usability, privacy, and future AI validation. No clinical or performance improvement is claimed without measured testing.},
keywords = {digital health; healthcare assistance; healthcare costs; hospital finder; scheme discovery; web application},
month = {October},
}