Gram Aarogya App: Offline First Android Telemedicine Platform for Rural Indian Healthcare

This paper presented Gram Aarogya, an Android telemedicine prototype designed for the rural healthcare-access context described for Nabha, Punjab. The system combines Kotlin and Jetpack Compose, Room over SQLite, Firebase integration, clinic discovery and Gemini-based symptom screening, dietary guidance and report explanation. The functional checks are working and AI prompts, safety/security measures, Firebase integration and dietary guidance have been added. These results demonstrate our current working prototype condition, not on-field deployment. The future scope includes documentation of more intensive test cases and outcomes, validation of security measures and synchronization, checking of AI outputs by professionals, completion of any remaining localization and role based workflows, and evaluation of practical use with real users in rural regions. Gram Aarogya's guiding principle remains that digital tools can support access to information and care coordination but must not replace qualified healthcare professionals.

[1] S. Sood et al., “Adoption and utilization of India’s eSanjeevani national telemedicine service,” Oxford Open Digital Health, vol. 3, art. oqaf025, 2025, doi: 10.1093/oodh/oqaf025. [2] M. Kleppmann, A. Wiggins, P. van Hardenberg, and M. McGranaghan, “Local-first software: you own your data, in spite of the cloud,” in Proc. ACM SIGPLAN Onward! 2019, Athens, Greece, Oct. 2019, pp. 154–178, doi: 10.1145/3359591.3359737. [3] H. Ashista et al., “An offline-first electronic health record for vulnerable populations: A mixed-methods feasibility study,” PLOS Digital Health, vol. 5, no. 2, art. e0001204, 2026, doi: 10.1371/journal.pdig.0001204. [4] B. Mingole, A. Majumdar, F. A. Choudhury, J. L. Kraschnewski, S. S. Sundar, and A. Yadav, “Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases,” arXiv:2506.13805, 2025. [5] E. Riboli-Sasco et al., “Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review,” J. Med. Internet Res., vol. 25, art. e43803, 2023, doi: 10.2196/43803. [6] E. A. Tarim and H. C. Tekin, “Performance evaluation of WebRTC-based online consultation platform,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 27, no. 6, pp. 4314–4327, 2019, doi: 10.3906/elk-1903-44. [7] I. Medhi, S. Patnaik, E. Brunskill, S. N. N. Gautama, W. Thies, and K. Toyama, “Designing Mobile Interfaces for Novice and Low-Literacy Users,” ACM Transactions on Computer-Human Interaction, vol. 18, no. 1, art. 2, 2011, doi: 10.1145/1959022.1959024. [8] P. Shah, N. Madhiwala, S. Shah, G. Desai, K. Dave, N. Dholakia, S. Patel, S. Desai, and D. Modi, “High uptake of an innovative mobile phone application among community health workers in rural India: An implementation study,” National Medical Journal of India, vol. 32, no. 5, pp. 262–269, 2019, doi: 10.4103/0970-258X.295956. [9] S. Prinja, P. Bahuguna, J. P. Tripathy, and R. Kumar, “Availability of medicines in public sector health facilities of two North Indian States,” BMC Pharmacol. Toxicol., vol. 16, art. 43, 2015, doi: 10.1186/s40360-015-0043-8. [10] E. Rajkumar et al., “Applications, benefits and challenges of telehealth in India during COVID-19 pandemic and beyond: a systematic review,” BMC Health Serv. Res., vol. 23, art. 7, 2023, doi: 10.1186/s12913-022-08970-8.