Guillaume Bouzillé1, Canelle Poirier2, Boris Campillo-Gimenez3, Marie-Laure Aubert4, Mélanie Chabot4, Emmanuel Chazard5, Audrey Lavenu6, Marc Cuggia7. 1. INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France; CHU Rennes, CIC Inserm 1414, Rennes, F-35000, France; CHU Rennes, Centre de Données Cliniques, Rennes, F-35000, France. Electronic address: guillaume.bouzille@chu-rennes.fr. 2. INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France; Université de Rennes 2, IRMAR, Rennes, F-35000, France. 3. INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France. 4. Université de Rennes 2, IRMAR, Rennes, F-35000, France. 5. Département de Santé Publique, Université de Lille EA 2694, CHU Lille, F-59000 Lille, France. 6. CHU Rennes, CIC Inserm 1414, Rennes, F-35000, France; Université Rennes 1, Rennes, F-35000, France. 7. INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France; CHU Rennes, CIC Inserm 1414, Rennes, F-35000, France; CHU Rennes, Centre de Données Cliniques, Rennes, F-35000, France.
Abstract
BACKGROUND AND 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.
BACKGROUND AND 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.