Predictive Tracking of Patient Flow in the Emergency Services During the Virus Winter Epidemics

Study Overview
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Brief Summary
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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.

Official Title
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Predictive Tracking of Patient Flow in the Emergency Services During the Virus Winter Epidemics

Conditions
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Disease Outbreaks, Child, Elderly, Bronchiolitis, Acute Renal Failure

Intervention / Treatment
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* Other: data retrieval


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: child < 24 months with bronchiolitis elderly < 60 years with acute renal failure or breathing problem Exclusion Criteria: refuse of transmission of their data

Ages Eligible for Study
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Minimum Age: 1 Month

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| patients < 24 months or 60 years with bronchiolitis or ARF<br>ARF = Acute Renal Failure | Other: data retrieval<br>* data retrieval with the Hospital Information System<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| build a predictive tool | 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. | at inclusion | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| Comparison virological databases with clinical diagnosis of patients |  | at inclusion | 
| Percentage of elderly staying more than 10 hours in the emergency services. |  | at inclusion |