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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
From Clinical Data Warehouses to Epidemiological Indicators: A Methodology for Real-Time Surveillance
Thomas Jaylet1, Amélie Brejot1, Valérie Bertaud1
1Université de Rennes, CHU Rennes, Inserm LTSI-UMR 1099, 35000 Rennes, France.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Clinical data warehouses (CDWs) enhance infectious disease surveillance by integrating diverse data streams. Combining these with advanced analytics improves outbreak detection and forecasting accuracy.
Area of Science:
- Epidemiology
- Health Informatics
- Data Science
Background:
- Clinical data warehouses (CDWs) leverage electronic health records for real-world epidemiological indicator generation.
- These infrastructures facilitate near real-time monitoring of infectious diseases using routinely collected hospital data.
- Integrating external data streams like web searches and meteorological variables can augment CDW capabilities.
Purpose of the Study:
- To enhance infectious disease surveillance by integrating novel data streams and advanced analytical methods.
- To apply these combined methods to a local CDW, considering changes in open data.
- To improve the timeliness and accuracy of infectious disease outbreak detection and forecasting.
Main Methods:
- Utilizing clinical data warehouses (CDWs) and electronic health records (EHRs).
- Incorporating external data streams such as web search queries and meteorological data.
- Applying statistical models, natural language processing (NLP), and machine learning (ML) techniques.
- Analyzing changes in open data sources.
Main Results:
- Demonstrated enhancement in the timeliness and accuracy of infectious disease outbreak detection.
- Successful integration of heterogeneous data signals for improved epidemiological insights.
- Validation of novel methods applied to a local CDW.
Conclusions:
- The integration of diverse data sources and advanced analytical methods significantly improves infectious disease surveillance.
- Clinical data warehouses are a valuable foundation for real-world epidemiology when augmented with external data and modern techniques.
- Future work should focus on continuously adapting these methods to evolving open data landscapes.
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