FGM Track: A Web-Based Appointment and Payment System for Faye Gumabay-Magalona Dental Clinic using Rule-Based Algorithms and Integrated Data Analytics

Manual processes for appointment scheduling and payment tracking in small dental clinics often lead to human error, inefficiency, and reduced patient satisfaction. This developmental research introduces FGMTrack, a web-based appointment and payment management system designed to address these administrative challenges through digital automation. Grounded in the principles of system efficiency and workflow optimization, the study integrates a rule-based scheduling algorithm to detect and prevent double-bookings while automatically managing time-slot availability in real-time. In addition, its data analytics component provides actionable insights into appointment trends, unpaid balances, and overall operational performance, supporting data-driven decision-making for clinic administrators. The system applies the ARIMA (AutoRegressive Integrated Moving Average) model to forecast future revenue by analyzing past financial records and booking patterns. This predictive approach enhances the clinic’s ability to anticipate income variations, allocate resources effectively, and support data-driven management decisions (Box et al., 2016; Hyndman & Athanasopoulos, 2021; Chatfield, 2000).
The system was developed for the Faye Gumabay-Magalona Dental Clinic, which previously relied on manual scheduling and payment recording. Following a developmental design approach, the system was built using the Agile Software Development Life Cycle (SDLC) with HTML, CSS, JavaScript, PHP, and MySQL to ensure scalability and reliability. Evaluation surveys conducted by users, administrators, and IT experts, guided by the ISO/IEC 25010 software quality model, assessed the system's functional suitability, usability, and performance efficiency. Results showed high user satisfaction and better performance compared to manual methods, confirming FGMTrack’s effectiveness in improving administrative efficiency and service delivery. Future improvements will focus on mobile access, AI-driven analytics, and multi-branch support to enhance scalability and long-term sustainability.

FGMTrack, Rule-Based Algorithm, Web-Based System, ISO 25010

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