Improving the Efficiency of Outpatient Services at Benue State University Teaching Hospital using the Queuing Theory
Bibliographic record
Abstract
Introduction: Long client waiting time is a characteristic of poor performance of the health care delivery and is a major challenge for healthcare services all over the world, especially in developing countries. The study was aimed at developing a model that optimizes performance of the general outpatient department of the Benue State University Teaching Hospital, Makurdi, Benue State Nigeria. Methodology: Data was collected through observation and interviews with doctors at the general outpatient clinic of the Benue State University Teaching Hospital. The average number of clients seen per day was calculated by determining the average of daily attendants for five consecutive working days. The data obtained was used to create a five capacity scenarios using the queuing theory software. Result: The Average Daily Attendance (ADA) was 73.2 clients while the Average Daily Arrival Rate was 10.47 clients per hour. There were six doctors working on any given day in the clinic and a doctor spends an average of 16.2 minutes per patient, representing an average of 3.7 patients per hour. The model showed that the optimum system performance can be achieved with four doctors (with 70.7% server utilization rate, average of 1.065 clients on the queue and 0.102 hours waiting time). Conclusion: Four doctors working at the same time at the general outpatient clinic is required for optimal performance. The queuing theory should be used regularly at GOPD BSUTH and in all health facilities experiencing long queues to optimize operational efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".