Web Based Predictive Inventory Healthcare Management System Using Eclat Algorithm and Hierarchical Clustering

Governments and health care decision-makers must encourage the creation of effective and efficient healthcare systems in view of the alarming rise in global health care expenditures and the rising demand for medical facilities (Ahmadi-Javid, Jalali, and Klassen, 2017). The efficient management of inventory resources is crucial to the clinics’ capacity to provide timely, high-quality healthcare via online clinics. According to Luciano et al. (2023), inventory management systems are essential in the medical industry because they prevent shortages of crucial medical supplies and needless purchases of excess inventory, both of which could jeopardize patient care. In urgent care centers, machine learning can assist in predicting patient flow (Maddigan & Sušnjak, 2023). This research has shown that using ensemble-based models instead of time-series forecasting is more likely to yield reliable performance results. Furthermore, in situations without contracts, the forecast analysis validated the predictive power of certain transactional parameters, like the volume and frequency of prior transactions. By using the Hierarchical Clustering technique to find trends in patient visits and help manage supply levels in the clinic, the current study attempts to give an overview of a web-based clinic demand forecasting system. The project’s goals are to provide a clinical information system that can forecast healthcare service supply and demand, optimize resource use to raise patient care standards and operational effectiveness, and provide a single system that can be tailored to various healthcare settings. To fully satisfy the needs of this investigation, the project used a triangulated strategy. With the quantitative approach, 50 respondents were given surveys and questionnaires to complete to gather numerical data. To understand users’ experiences with a particular system, the qualitative approach mainly focuses on obtaining information through focus groups, interviews, and answering open-ended questions. Primary data from system administrators and users, as well as secondary data from books, websites, and articles, make up the methods used to obtain the data. When creating the system, the project adheres to the Agile Software Development Life Cycle (SDLC) process, which includes several phases such as planning, analysis, design, development, testing, implementation, and maintenance. Within the database’s structure, data scalability, security, and consistency are also preserved. The system was evaluated using ISO 25010, which uses a 4-point Likert-type scale, weighted mean, and percentage analysis to evaluate five perspectives: robustness, usability, effectiveness, and reliability. Both the user and technical respondents expressed satisfaction with the system’s use and portability. With an average mean score of 3.21 overall, users gave these aspects positive ratings. With an overall mean score of 3.28, technical responders commended portability and performance efficiency. These results demonstrate that, the system is reliable and meets the needs of its users. Its focus on adaptability and usability makes it a useful tool for managing visitor data, which enhances the user experience.