Aim/PurposeTo develop a Deep Fuzzy Logic (DFL) model linking all hospital departments –inpatient, outpatient, and A&E. To maximize staff distribution and bed capacity using linear optimization techniques.

BackgroundGrowing pressure on healthcare systems calls for efficient use of resources when extra money is not accessible. Rising demand and limited capacity in every field of medicine need comprehensive modeling for hospitals.

MethodologyWe developed a DFL model combining outpatient, hospital, and A&E services. This model estimates demand for all specialties, considers patient pathway uncertainty, and projects required bed capacity and staff demands by linear optimization using discrete event simulation.

ContributionComplete hospital models are rare; recent studies demonstrate the rising use of numerous operational research (OR) techniques. Deep Fuzzy Logic (DFL) models are becoming more and more interesting when combined with optimization, simulation, and forecasting.

FindingsThe model provides a means of short-term and long-term strategic planning decision support to crucial decision-makers. Our DFL model showed a 15% gain in bed utilization efficiency and a 10% drop in staff shortages compared to more traditional methods.

Recommendation for ResearchersInvestigations should prioritize the validation of the algorithm in various healthcare environments to assess its efficacy in clinical application.

Future ResearchIn future research, this work can be enhanced using several deep learning algorithms to achieve better accuracy and performance.