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@article{174394,
        author = {Janani N and Dhanusri S and Kalagavathi S and Uma S},
        title = {Centralized PHC Monitoring and Management System using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3897-3901},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174394},
        abstract = {This system is designed to centralize the operations of Primary Health Centers (PHCs), Upgraded PHCs, and Subcenters, integrating them with the District Deputy Director of Health Services (DDHS) for real-time monitoring. By leveraging advanced technologies, the system offers a comprehensive solution for efficient healthcare management. One of its key features is the tracking of doctor attendance using face recognition technology, which ensures accurate marking of attendance and generates automated absenteeism alerts for better workforce management.
The system also facilitates PHC monitoring by maintaining detailed patient information and generating daily update forms. These updates are automatically analyzed and presented in graphical formats, allowing for clear insights into service delivery and patient care.
Furthermore, the system enables seamless communication by sending daily or monthly reports to the DDHS, supporting effective decision-making and improving overall healthcare management. By centralizing and automating critical functions, this system enhances operational efficiency, accountability, and the quality of healthcare services.},
        keywords = {Face Recognition Attendance, Automated alert, PHC center Integration, Anomaly detection.},
        month = {March},
        }