Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• Accurate real-time monitoring of influenza outbreaks is crucial for public health.
• Informed decision-making by officials can help mitigate the impact of flu.
• Traditional surveillance methods may have limitations in providing timely data.

Purpose of the Study:

• To evaluate the potential of using electronic health records (EHRs) for real-time influenza monitoring.
• To assess the accuracy and reliability of machine learning techniques in estimating flu outbreaks.
• To provide near real-time regional flu outbreak data for the United States.

Main Methods:

• Extracting information from cloud-based EHR databases.
• Applying machine learning algorithms to analyze EHR data.
• Integrating historical epidemiological data for model training and validation.

Main Results:

• Demonstrated the potential for accurate and reliable near real-time regional flu outbreak estimates.
• Showcased the effectiveness of combining EHR data with machine learning.
• Validated the approach using historical epidemiological information.

Conclusions:

• Cloud-based EHR data, coupled with machine learning, offers a promising tool for real-time flu surveillance.
• This methodology can significantly enhance public health preparedness and response to influenza outbreaks.
• Near real-time regional estimates can empower officials to make more informed and timely interventions.