Forecasting Outpatient Visits: Leveraging Genetic Fuzzy Systems for Enhanced Healthcare Management at Efarina Etaham Berastagi Hospital
Keywords:
Genetic Fuzzy Systems, Healthcare Management, Outpatient Visits, Predictive Modeling, Resource AllocationAbstract
The efficient management of patient influx within healthcare facilities poses a critical challenge, necessitating precise forecasting and resource allocation. This study explores the predictive modeling of outpatient visits at Efarina Etaham Berastagi Hospital employing the innovative Genetic Fuzzy Systems (GFS) method. Harnessing the synergy between genetic algorithms and fuzzy logic, this research endeavors to develop a predictive model capable of accurately anticipating the fluctuating patterns of outpatient visits. The study amalgamates historical visit records, patient demographic data, and temporal factors within the GFS framework, aiming to optimize resource allocation, refine scheduling strategies, and elevate patient care delivery. The methodology involves the integration of genetic algorithms to iteratively evolve the predictive model and fuzzy logic to handle uncertainties inherent in healthcare datasets. The model's performance is evaluated through rigorous analysis, validation against actual visitation data, and comparison against established metrics to ascertain its accuracy and reliability. The outcomes of this research unveil a predictive model capable of forecasting outpatient visits with notable accuracy, showcasing the potential of the GFS method in enhancing healthcare management.
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