Emergency Department Patient Flow Simulations Using Spreadsheets
Bibliographic record
Abstract
INTRODUCTION: Patient flow computer simulations allow Emergency Department stakeholders to assess operational interventions, develop utilization and performance measures, and produce estimates for budgeting or planning purposes. Key challenges of traditional discrete-event computer simulation software are their inherent complexity for modeling, coding, or analyzing output and their significant costs and training. We propose a simulation platform that runs in spreadsheets. Because of their low cost, popularity and powerful functionality and performance, spreadsheets also allow for the development and management of simulations that efficiently output results that are just as reliable as those from traditional software. METHODS: A spreadsheet simulation is developed by modeling one row as one simulated minute (more than 20,000 rows for a 2-week period). Uncertainty in arrivals, patient type, routing, and treatment times is modeled using the "rand()" function to simulate the state of the Emergency Department at a given point in time. The patient is tracked with embedded "if()" functions and summary statistics are obtained through range functions. We use an equivalence test to determine whether the resulting average length-of-stay figures are the same as those of a traditional simulation platform. RESULTS: We find little significant difference in average length-of-stay figures between both models. CONCLUSIONS: Spreadsheet simulations are as effective as traditional simulations but easier to use, understand, and implement. Spreadsheet software is widely available, at a fraction of the cost of discrete-event simulation software. Coding spreadsheet simulations may be more challenging as it requires a different and more novel expertise than traditional computer programming. However, spreadsheets can be organized to reference existing datasets, thus minimizing the burden of copying and likelihood of transcription errors and information leakage. Output analysis can also be customized with user-specific performance statistics and charts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".