The Impact of Opening a Private Hospital Emergency Department on the Hospital and Patient Characteristics
Bibliographic record
Abstract
Introduction: Australian public hospital emergency departments (EDs) are under increasing pressure with higher patient volumes, failure to meet triage target times and increased ambulance wait times. Further, there is limited literature exploring how the opening of an ED affects Australian hospitals. This study focuses on Hollywood Private Hospital (HPH), a large private hospital in Western Australian which opened their ED in November 2021. The research aimed to examine how the introduction of the ED influenced the hospital’s service demands and resourcing. Method: This study investigated hospital inpatient characteristics including admissions, clinical deterioration episodes, deaths, after-hours theatre activity and companion care hours. The investigation compared the periods 01 January to 30 June 2021 (2021) and 01 January to 30 June 2022 (2022) to identify differences pre and post the ED opening. Results: Overall, the number of inpatient admissions was similar from 2021 to 2022 (31,061 and 31,706 respectively). However, there was a statistically significant change in the admission type with a decrease in elective admissions (925.2 in 2021 and 880.4 in 2022 (p<.001)) and an increase in emergency admissions (67.4 in 2021 and 111.1 in 2022 (p<.001)). A significantly higher incidence rate of rapid response calls was reported in 2022 compared to 2021 (p=.043), nonetheless there was no difference in the incidence rates of cardiac arrest (p=.445), code blacks (p=.600) or patient deaths (p=.880). From 2021 to 2022 there was an increase in both after-hours theatre procedures (6.6% to 9.0%; χ2=50.9 p<.001) and median companion care hours (Md = 32.5 to Md=56.3; U=2.3, p=.021). Implications: The opening of the HPH ED resulted in increased after-hours and emergency related admissions with a co-occurring increase on companion hours. These impacts necessitate significant resourcing investment such as revised staffing models and rosters, additional recruitment, and change management.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".