HOSPITAL RESOURCE OPTIMIZATION USING BIG DATA ANALYTICS AND MACHINE LEARNING

The study has developed an advanced system that optimizes the use of resources within hospitals via the application of machine learning and Big Data analysis techniques. In the current study, the proposed system can easily integrate and combine the available data about hospital operations, including admissions, intensive care unit management, personnel scheduling, and operation theater management.   Through the use of various machine learning algorithms, the system can successfully make accurate predictions regarding future resource needs in terms of patient beds, ICU occupancy rate, and number of personnel required. The process of incorporating data pre-processing methods ensures the provision of high-quality and reliable data that makes predictions more accurate. The outcomes generated by the model clearly show its ability to identify the trends regarding patients and forecast peaks during which demand increases. The comparison between the system and manual method proves that the proposed system is more accurate and fast compared to conventional methods used at most facilities. Consequently, hospitals will be able to change their approaches to become proactive in decision making.   Moreover, the system increases efficiency due to optimized resource allocation and balance of staff workloads, resulting in high-quality health care services and satisfied patients. The system is designed in such a way that it can be easily scaled to accommodate various conditions in hospitals and can be enhanced through innovative technologies in the future.

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