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This research aims to evaluate outpatient flow dynamics in a Nigerian hospital using queueing theory integrated with machine learning.

June 7, 2026Open Access

Modeling and Analysis of Outpatient Flow Dynamics: A Queueing Theory and Machine Learning Approach in a Nigerian Hospital

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This research aims to evaluate outpatient flow dynamics in a Nigerian hospital using queueing theory integrated with machine learning.
Evaluated the M/M/S queueing model for outpatient departments in a Nigerian hospital over a two-week period.
Collected primary data on patient arrivals and service times during peak hours (9:00 a.m.–2:00 p.m.).
Developed a Random Forest regressor and Support Vector Machine classifier to model arrival patterns and demand levels.
The M/M/S queue model revealed critical overload with a utilization rate (ρ) of 1.16.
Kolmogorov–Smirnov tests indicated that the exponential distribution assumptions were invalid, compromising model accuracy.
The Random Forest regressor achieved R² = 0.89, while the Support Vector Machine classifier reached an accuracy of 86%.

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Randomized trial evaluates outpatient flow dynamics in a Nigerian hospital, suggesting a hybrid analysis approach.

Okoro et al. (2026) studied this question.

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