Sanavi - Your Health Partner

Abstract

The rapid advancement of Artificial Intelligence (AI) in healthcare has enabled the development of intelligent systems that improve diagnosis accuracy, patient management, and emergency response. Sanavi – Your Health Partner is an integrated AI-based healthcare platform designed to address key challenges such as delayed diagnosis, inefficient OPD management, improper identification during emergencies, and lack of real-time emergency coordination. The system incorporates fingerprint-based blood group identification, online OPD appointment booking, AI-assisted medical report analysis, and smart ambulance integration with real-time tracking. By combining biometric identification, digital healthcare services, and intelligent decision support, Sanavi enhances hospital workflow efficiency, reduces waiting time, and supports timely medical intervention. This paper presents the architecture, methodology, features, and applications of Sanavi, demonstrating a practical approach toward modern, accessible, and secure digital healthcare solutions.

Introduction

The text presents Sanavi – Your Health Partner, an AI-driven, integrated healthcare platform designed to address common challenges in modern healthcare systems such as delayed diagnosis, overcrowded outpatient departments, inaccurate patient identification during emergencies, and inefficient emergency response. These issues are especially critical in high-population regions with limited healthcare resources. Sanavi emphasizes automation, accuracy, and user-centric design, making it suitable for hospitals, clinics, and emergency medical services.

The literature review identifies that existing healthcare solutions focus on isolated functions like appointment booking, disease prediction, or ambulance tracking, lacking seamless integration. While technologies such as fingerprint-based blood group detection, online OPD systems, and smart ambulances show promise, they are often implemented independently. This fragmentation highlights the need for a unified healthcare platform, which Sanavi aims to provide.

The problem statement underscores the risks posed by fragmented healthcare systems, including errors in blood group identification, long OPD waiting times, and poor emergency coordination. Sanavi addresses these issues through a centralized, secure, and intelligent AI-based framework.

The methodology outlines a modular system architecture integrating biometric blood group identification using fingerprint analysis and CNN models, online OPD appointment scheduling with queue management and emergency prioritization, AI-assisted medical report analysis using NLP and deep learning, and real-time smart ambulance tracking with GPS and route optimization.

Results indicate improved hospital efficiency, reduced patient waiting times, faster emergency response, and enhanced diagnostic support. The platform’s integrated design enables smooth coordination among patients, doctors, and administrators. While currently a prototype with limited datasets, Sanavi demonstrates strong potential. Future work includes large-scale deployment, deeper hospital database integration, advanced disease prediction, live ambulance–hospital communication, and mobile app support.

Conclusion

Sanavi – Your Health Partner presents a comprehensive AI-driven healthcare management system that addresses critical challenges in modern healthcare delivery. By integrating biometric identification, digital OPD management, AI-assisted diagnostics, and smart ambulance tracking, the system improves efficiency, accuracy, and emergency response. The proposed platform provides a scalable foundation for future intelligent healthcare solutions and demonstrates the effective use of AI in enhancing patient care and hospital operations.

References

[1] M. Prashanthi, “Blood Group Detection with Fingerprint Using Deep Learning,” International Journal of Scientific Research in Engineering and Management (IJSREM), vol. 09, no. 11, Nov. 2025. doi: 10.55041/IJSREM54579. [2] Vaidya, Sanivarapu Prasanth & Tyagi, Akansha & S., Ashok & G., Naga & R., Meenakshi. (2025). Advancements in hemotype identification: Fingerprint analysis for blood group determination. Franklin Open. 13. 100429. 10.1016/j.fraope.2025.100429. [3] Bhaskarwar, Roshani & Das, Pius & Gaikar, Suraj & Chavan, Chitralekha & Jog, Swarangi. (2025). Online Doctor Appointment using Machine Learning. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. 09. 1-9. 10.55041/IJSREM42822. [4] Prof. Jeni Naarayanan L A, S.Manoj, M.Praveen, M.Johnson, S.Shyam, 2024, Smart Artificial Intellligence Ambulance with Decision Making System, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 13, Issue 04 (April 2024). [5] S. Natarajan, J. Harirajkumar, S. Santhosh and R. Srinith, \"Online Hospital Appointment Booking,\" 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2024, pp. 1-4, doi: 10.1109/ICONSTEM60960.2024.10568655. [6] S. Pankaj, A. Tomar, Y. Poojashree, J. Saurabh, S. Bhatambrekar and S. Awasthi, \"The Study of Online Appointment System - A Case Study,\" 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India, 2022, pp. 590-595, doi: 10.1109/CISES54857.2022.9844363. [7] S. Bensbih, O. Bouksour and S. Rifai, \"On line appointment systems in a patient Centric Strategy: a qualitative approach in a case study for hospitals in Morocco,\" 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), Paris, France, 2019, pp. 1735-1739, doi: 10.1109/CoDIT.2019.8820672. [8] V. Akshay, A. Kumar S., R. M. Alagappan and S. Gnanavel, \"BOOKAZOR - an Online Appointment Booking System,\" 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), Vellore, India, 2019, pp. 1-6, doi: 10.1109/ViTECoN.2019.8899460. [9] Ruchit, P. Suryavanshi, S. Kaushik and D. Dev, \"An Automated Model for Booking Appointment in Health Care Sector,\" 2021 International Conference on Technological Advancements and Innovations (ICTAI), Tashkent, Uzbekistan, 2021, pp. 7-10, doi: 10.1109/ICTAI53825.2021.9673206. [10] C. Thaijiam, \"A Smart Ambulance With Information System and Decision-Making Process for Enhancing Rescue Efficiency,\" in IEEE Internet of Things Journal, vol. 10, no. 8, pp. 7293-7302, 15 April15, 2023, doi: 10.1109/JIOT.2022.3228779. [11] M. A. R. Abdeen, M. H. Ahmed, H. Seliem, T. R. Sheltami, T. M. Alghamdi and M. El-Nainay, \"A Novel Smart Ambulance System—Algorithm Design, Modeling, and Performance Analysis,\" in IEEE Access, vol. 10, pp. 42656-42672, 2022, doi: 10.1109/ACCESS.2022.3168736. [12] Sanjana, I & Wihastuti, Titin & Muslihah, Nurul. (2021). The Ambulance Location Can Influence Emergency Medical Service Response Time: A Literature Review. Research Journal of Life Science. 8. 166-172. 10.21776/ub.rjls.2021.008.03.6. [13] P. Thirumoorthi, M. Deeparasi, J. Hariprakash, A. M and K. Premalatha, \"Development of Smart System for Ambulance,\" 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2021, pp. 1-5, doi: 10.1109/ICAECA52838.2021.9675577. [14] V. N. Patil and D. R. Ingle, “A Novel Approach for ABO Blood Group Prediction using Fingerprint through Optimized Convolutional Neural Network”, Int J Intell Syst Appl Eng, vol. 10, no. 1, pp. 60–68, Mar. 2022. [15] T. Nihar, K. Yeswanth and K. Prabhakar MATEC Web Conf., 392 (2024) 01069 DOI: https://doi.org/10.1051/matecconf/202439201069. [16] Prasad, M., & Amrutha. (2023). Blood group detection through fingerprint images using Image processing. International Journal for Research in Applied Science and Engineering Technology (IJRASET). https://doi.org/10.22214/ijraset.2023.54878

Copyright

Copyright © 2025 Rashmi V, Jnaneshwari R, Krupa U B, Kruthi U B, Likitha S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.