Title :

Leveraging hospital big data to monitor flu epidemics

Author(s) :

Bouzille, Guillaume [Auteur]
Centre Hospitalier Universitaire [Rennes]
Poirier, Canelle [Auteur]
Campillo-Gimenez, Boris [Auteur]
Aubert, Marie-Laure [Auteur]
Chabot, Melanie [Auteur]
Chazard, Emmanuel [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Lavenu, Audrey [Auteur]
Cuggia, Marc [Auteur]

Journal title :

Computer Methods and Programs in Biomedicine

Abbreviated title :

Comput. Meth. Programs Biomed.

Volume number :

154

Pages :

153-160

Publisher :

Elsevier

Publication date :

2018-02-01

ISSN :

0169-2607

English keyword(s) :

Clinical data warehouse
Information retrieval system
Sentinel surveillance
Influenza
Health Information Systems
Health big data

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

OBJECTIVE: Influenza epidemics are a major public health concern and require a costly and time-consuming surveillance system at different geographical scales. The main challenge is being able to predict epidemics. Besides ...
Show more >OBJECTIVE: Influenza epidemics are a major public health concern and require a costly and time-consuming surveillance system at different geographical scales. The main challenge is being able to predict epidemics. Besides traditional surveillance systems, such as the French Sentinel network, several studies proposed prediction models based on internet-user activity. Here, we assessed the potential of hospital big data to monitor influenza epidemics. METHODS: We used the clinical data warehouse of the Academic Hospital of Rennes (France) and then built different queries to retrieve relevant information from electronic health records to gather weekly influenza-like illness activity. RESULTS: We found that the query most highly correlated with Sentinel network estimates was based on emergency reports concerning discharged patients with a final diagnosis of influenza (Pearson's correlation coefficient (PCC) of 0.931). The other tested queries were based on structured data (ICD-10 codes of influenza in Diagnosis-related Groups, and influenza PCR tests) and performed best (PCC of 0.981 and 0.953, respectively) during the flu season 2014-15. This suggests that both ICD-10 codes and PCR results are associated with severe epidemics. Finally, our approach allowed us to obtain additional patients' characteristics, such as the sex ratio or age groups, comparable with those from the Sentinel network. CONCLUSIONS: Conclusions: Hospital big data seem to have a great potential for monitoring influenza epidemics in near real-time. Such a method could constitute a complementary tool to standard surveillance systems by providing additional characteristics on the concerned population or by providing information earlier. This system could also be easily extended to other diseases with possible activity changes. Additional work is needed to assess the real efficacy of predictive models based on hospital big data to predict flu epidemics.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

CHU Lille
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2019-12-09T18:17:38Z
2024-04-03T08:44:51Z