Improving Patient Flow and Operational Efficiency in Emergency Rooms using a Discrete Event Simulation Approach
Bibliographic record
Abstract
Emergency rooms (ERs) are essential components of the healthcare system, but in recent times, ERs across Canada have been experiencing extended emergency room length of stay (ERLOS), patients leaving without being seen, and overcrowding. The purpose of this research is to address these issues by utilizing a discrete event simulation approach to improve patient flow and operational efficiency. We propose two policies and interventions that can help alleviate the pressure on ERs, improve operational efficiency, and reduce complications associated with delayed treatment. The first policy is an Acute Medical Unit which is hospital unit that is staffed and equipped to receive patients with acute medical illness and provide rapid assessment and treatment to emergency patients. The second policy is an On-Call Physician, a physician who is called when the number of active patients in ER exceeds twice the ER capacity and helps with the increased workload. To develop the simulation model and test the impact of proposed strategies, we use Rockwell Arena 16 and factor in real-life factors associated with ERs such as arrival rates, service times, and patient acuity levels. In addition, the impact of entry and access blocks to and from the ER is examined. An Entry Block prevents patients from accessing treatment in the ER as a result of a lack of capacity. An Access Block prevents patients from accessing a bed in the hospital itself. Both factors have a significant impact on ER operations and efficiency. Our findings indicate that the proposed intervention strategies can reduce the time patients spend waiting for treatment and the number of patients leaving without being seen. This reduces the complications associated with delayed treatment and addresses overcrowding in emergency rooms. Therefore, the proposed policies have the potential to improve patient flow and operational efficiency in ERs. These findings have significant implications for healthcare facilities as they can utilize this simulation model to test various resource planning strategies and make informed decisions to improve patient healthcare experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".