scholarly journals An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

10.2196/12341 ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. e12341 ◽  
Author(s):  
Cheng-Yi Yang ◽  
Ray-Jade Chen ◽  
Wan-Lin Chou ◽  
Yuarn-Jang Lee ◽  
Yu-Sheng Lo
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Influenza Surveillance ◽  
Early Prediction ◽  
Influenza Like Illness

scholarly journals An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation (Preprint)

2018 ◽  
Author(s):  
Cheng-Yi Yang ◽  
Ray-Jade Chen ◽  
Wan-Lin Chou ◽  
Yuarn-Jang Lee ◽  
Yu-Sheng Lo
Keyword(s):  
Health Care ◽  
Infectious Diseases ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Predictive Power ◽  
Influenza Surveillance ◽  
Early Prediction ◽  
Influenza Like Illness ◽  
Reporting Systems ◽  
Positive Results

BACKGROUND Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. OBJECTIVE We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. METHODS First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. RESULTS Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables—TMUHcS-RITP and TMUHcS-IMU—showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. CONCLUSIONS Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations.

scholarly journals Influenza‐like illness in Australia: A comparison of general practice surveillance system with electronic medical records

2020 ◽  
Vol 14 (6) ◽  
pp. 605-609
Author(s):  
Carla De Oliveira Bernardo ◽  
David Alejandro González‐Chica ◽  
Monique Chilver ◽  
Nigel Stocks
Keyword(s):  
General Practice ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Surveillance System ◽  
Influenza Like Illness

scholarly journals 629Influenza-like illness surveillance in Australia: comparison of general practice data with laboratory-confirmed influenza notifications

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Carla Bernardo ◽  
David Gonzalez ◽  
Nigel Stocks
Keyword(s):  
General Practice ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Research Network ◽  
Practice Research ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Australian General Practice ◽  
General Practices ◽  
Highly Correlated

Abstract Background Influenza is a respiratory infection responsible for 645,000 annual deaths worldwide. Surveillance systems provide valuable data for monitoring influenza in order to detect outbreaks and guide public health responses. This study aimed to investigate the epidemiology of influenza-like illness (ILI) using two Australian general practice databases (MedicineInsight and the Australian Sentinel Practice Research Network (ASPREN)) and compare them with laboratory-confirmed influenza from the National Notifiable Diseases Surveillance System (NNDSS). Methods All patients who had a consultation in MedicineInsight general practices or ASPREN and all laboratory-confirmed influenza reported by the NNDSS between 2015-2017 were included. Weekly ILI rates per 1,000 consultations (MedicineInsight/ASPREN) were compared with influenza notifications (NNDSS). Results Data was consistent among sources, with higher cases in 2017, among women and patients aged 20-49 years. The peak rate in MedicineInsight almost doubled in 2017 compared to 2015, while in ASPREN it was less pronounced. MedicineInsight ILI curves more closely resembled NNDSS patterns (shape, the start of the season, peaks) than ASPREN, although both were highly correlated with NNDSS (r = 0.90 to 0.97 and r = 0.88 to 0.98, respectively). Conclusions MedicineInsight and ASPREN provided consistent ILI results, both resembling confirmed influenza epidemic curves, suggesting the potential use of routinely collected electronic medical records (MedicineInsight) in influenza surveillance. MedicineInsight provides comprehensive medical data, such as underlying conditions, medications prescribed and vaccination status, which could be used to improve accuracy on influenza detection. Key messages Electronic medical records could be used to monitor ILI in combination with ASPREN for effective early detection of outbreaks.

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