A new approach to over-bedding in American healthcare: Statistical analysis and computer simulation of patient admission in a West Virginia state psychiatric hospital
Bibliographic record
Abstract
Objective: To identify the factors that have a substantial impact on a West Virginia state psychiatric hospital’s bed occupancy by investigating historical admission data, and developing a computer simulation system to give insight into modifiable variables that reduce admission numbers, therefore to provide solutions to the over-bedding problem.Methods: Quantitative review of hospital admission data from January 2007 to November 2013 allowed for the construction of a simulation model to estimate the inpatient flow. The system’s performance was evaluated after alteration of selected parameters and variables.Results: The study revealed significant regional differences in admission numbers. The civil commitments and psychiatric hospitalizations do not directly correlate with county coverage populations. Some counties sent disproportionately more patients. Patients’ length of stay also varied among geographical areas. Re-admission was not uncommon. Using the percentage of diversion as the outcome measurement, the computer simulation model reconstructed the admission scenario multiple times, predicting that the diversion rate can be significantly reduced if certain variables (hospital capacity, patient arrivals from top referring counties, and patient length of stay) are changed.Conclusions: Involuntary admissions were unevenly distributed according to geography and population in the studied American state psychiatric hospital. Using historical data, computer simulations can model hospital admission systems to evaluate performance and predict needs for change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".