Performance Evaluation of a M/G/1 Queue Model for Patient Flow in a Health Care System
Bibliographic record
Abstract
This study focuses on mitigating patient congestion in healthcare departments by employing an M/G/1 queue.The system comprises of two crucial servers, HR (Human Resource) and nurse and investigates the flow of patients between them.The HR department's role in managing staffing and generating daily census reports significantly reduces nursing demand and congestion, directing patients to the nursing unit for intensive medical care.The system demonstrates stability when the HR's arrival rate is lower than its service rate, effectively reducing congestion.Utilizing the Matrix-Geometric method ensures system stability, crucial for efficient healthcare operations.The hidden Markov model, supported by the Viterbi algorithm, facilitates the determination of the most efficient HR and nurse sequencing and necessary staffing levels, accommodating sequences of any length.The novelty of the work lies in the choice of Viterbi algorithm in modelling as its computational complexity O(n)=O( 6)=O(1).The hidden Markov model and Baum-Welch algorithm offer a comprehensive analysis of patient flow dynamics, unveiling hidden states and transitions that influence system performance.This comprehensive understanding aids in managing overcrowding and optimizing resource utilization.Presenting the results numerically in tables provides a holistic view of healthcare department dynamics, contributing to effective process improvements and resource allocation.This study's innovative integration of methodologies offers a sophisticated approach to understanding and optimizing the dynamics of patient flow in healthcare departments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".