Forecasting ED Overcrowding With Statistical Methods: A Prospective Validation Study
试验速览
- 阶段
- 不适用
- 入组人数
- 160,000
- 主要终点
- Next day overcrowding
研究概览
简要总结
The aim of this study is to prospectively validate statistical forecasting tools that have been widely used retrospectively in forecasting ED overcrowding
详细描述
Emergency department (ED) overcrowding is a chronic international issue that has been repeatedly associated with detrimental treatment outcomes such increased 10-day-mortality. Forecasting future overcrowding would enable pre-emptive staffing decisions that could alleviate or prevent overcrowding along with its detrimental effects.
Over the years, several predictive algorithms have been proposed ranging from generalized linear models to state space models and, more recently, deep learning algorithms. However, the performance of these algorithms has only been reported retrospectively and the clinically significant accuracy of these algorithms remains unclear.
In this study the investigators aim to investigate the accuracy of the previously reported ED forecasting algorithms in a prospective setting analogous to the way these tools would be used if used implemented as a decision-support system in a real-life clinical setting.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 16 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •All patients presenting in the Emergency Department
排除标准
- •No exclusion criteria
结局指标
主要结局
Next day overcrowding
时间窗: 24 hours
A day is defined as overcrowded if daily peak occupancy exceeds 80 patients, and severely overcrowded if daily peak occupancy exceeds 100 patients.
次要结局
- Number of hourly arrivals in the ED 24 hours ahead(24 hour)
- Hourly occupancy in the ED 24 hours ahead(24 hour)
- Number of daily arrivals in the ED 7 days ahead(24 hour)
- Daily peak occupancy in the ED 7 days ahead(24 hours)