Design and Implementation of Smart Doctor Appointment and Healthcare Management System using NLP Chatbot
DOI:
https://doi.org/10.15662/IJEETR.2026.0802165Keywords:
Healthcare System, Appointment Scheduling, Chatbot, Digital Prescription, Doctor Availability, Doctor AvailabilityMedical Record ManagementAbstract
: The rapid growth of digital healthcare technologies has created a demand for intelligent systems that improve communication and coordination between patients and healthcare providers. Traditional hospital workflows often rely on manual appointment booking, paper-based prescriptions, and fragmented coordination between doctors, laboratories, and pharmacies. These methods frequently result in inefficiencies, long waiting times, scheduling conflicts, and increased administrative workload for hospital staff. This research proposes an AI-based smart healthcare appointment and prescription management system integrated with a chatbot assistant to streamline healthcare service delivery. The system enables patients to register online, check doctor availability, and schedule appointments through a user-friendly web-based interface. Patients can view doctor specialization, consultation timings, and available appointment slots before confirming their booking. Doctors can manage appointment requests, access patient details, conduct consultations, and generate digital prescriptions that can be downloaded by patients for future reference. The system further supports pharmacy verification and laboratory test coordination, ensuring proper communication between healthcare professionals and accurate management of medical records. A chatbot module assists patients by responding to common healthcare queries and recommending appropriate specialists based on symptoms. This interactive feature improves user engagement and helps patients quickly access healthcare services. Additionally, the system incorporates automated notifications and reminders to inform patients about appointment confirmations, cancellations, and upcoming consultation schedules. These reminders help reduce missed appointments and improve hospital efficiency. The platform is implemented using the Django web framework for backend development and Flask for chatbot integration, while SQLite database management ensures efficient data storage, retrieval, and security of patient records. The proposed system enhances healthcare accessibility, reduces manual administrative workload, and improves patient experience by providing a centralized digital healthcare platform. Performance evaluation demonstrates that the system operates efficiently under moderate workloads and provides a scalable architecture for future healthcare digitalization and integration with advanced artificial intelligence technologies
References
1. S. Chakraborty et al., ”An AI-Based Medical Chatbot Model for Infectious Disease Prediction,” IEEE Access, vol. 11, pp. 128470– 128483, 2023.
2. M. Bali et al., ”Diabot: A Predictive Medical Chatbot using Ensemble Learning,” IJRTE, vol. 8, no. 2, 2019.
3. N. V. Shinde et al., ”Healthcare Chatbot System using Artificial Intelligence,” in ICOEI, 2021.
4. Y. Sharma and D. Jain, ”Intelligent Conversational Healthcare Chat-Bot System,” IJSREM, 2022.
5. P. Anandan et al., ”AI-based Chat Bot for Patient Health Care,” in ICCCI, 2022.
6. S. Swain et al., ”Healthcare Chatbot System: A Survey,” in ICIEM, 2022.
7. U. Shaikh et al., ”AI Healthcare Chatbot,” IJSMS, 2025.
8. T. Rathod et al., ”Intelligent Chatbot for Healthcare Assistant,” IJFMR, 2023.
9. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
10. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
11. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
12. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
13. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
14. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
15. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
16. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
17. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
18. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
19. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
20. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
21. D. J. Thakre et al., ”Encrypted Cloud-Based Health Appointment System Using AI,” 2025.
22. M. Nasr et al., ”Smart Healthcare in the Age of AI,” IEEE Access, 2021.
23. R. F. Mansour et al., ”AI and IoT Enabled Disease Diagnosis Model,” IEEE Access, 2021
24. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
25. Vimal, V. R., Jayalakshmi, D., Narayanan, L. K., Hemavathi, R., & Loganayagi, S. (2024, November). 5G-Enabled Remote Healthcare Monitoring for Improved Patient Care. In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET) (pp. 1-5). IEEE.
26. Anbazhagan, K. (2025). AI Driven Zero Trust Security Model for Enterprise Data Protection and Intelligent Infrastructure Management. International Journal of Technology, Management and Humanities, 11(03), 101-107.
27. Sugumar, R. (2025). Federated AI in Offline-First Mobile Health Architectures for Privacy-Preserving Clinical Intelligence. International Journal of Science, Research and Technology, 8(4), 14589-14600.
28. Gopinathan, V. R. (2024). Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking. International Journal of Computer Technology and Electronics Communication, 7(6), 9837-9845.
