Predictive Tracking of Patient Flow in the Emergency Services During the Virus Winter Epidemics
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 760,000
- 试验地点
- 11
- 主要终点
- build a predictive tool
研究概览
简要总结
Epidemics and infectious diseases in general, punctuate much of the activity of an emergency service. The impact of winter infections is particularly important to vulnerable populations such as infant during bronchiolitis epidemics and the elderly during seasonal influenza. Each year, these epidemic phenomena lead to disorganization of emergency services and healthcare teams by lack of anticipation and organizational measures in particular to manage the approval of emergency services for the most vulnerable populations requiring hospitalization.
For 2 years, the pediatric emergency department of St Etienne University Hospital has a decision support tool for the periods of winter epidemics. Through a retrospective analysis of Passages of Emergency summary, this tool provides an estimate of infants with bronchiolitis flow day to day, and the availability in real time of an abnormally high flow of patients to pediatric emergencies. These data can help to affirm that the epidemic begins in this hospital.
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
- 1 Month 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •child < 24 months with bronchiolitis
- •elderly < 60 years with acute renal failure or breathing problem
排除标准
- •refuse of transmission of their data
结局指标
主要结局
build a predictive tool
时间窗: at inclusion
a tool with different levels of alerts of the influx of people aged to emergencies during winter epidemics. Variables in the model : activity database in emergency services, computer data, virology database and average length of stay.
次要结局
- Comparison virological databases with clinical diagnosis of patients(at inclusion)
- Difference between the estimated date and the effective date of the activity peak on the average length of stay of patients in the hospital of Saint Etienne(at inclusion)
- Difference between the estimated date and the effective date of the activity peak on the average length of stay of patients in the other hospitals(at inclusion)
- Percentage of elderly staying more than 10 hours in the emergency services.(at inclusion)