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Automatic case cluster detection using hospital electronic health record data.

Michael E DeWitt1, Thomas F Wierzba1

  • 1Department of Internal Medicine, Section on Infectious Disease, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.

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Summary
This summary is machine-generated.

This study introduces an algorithm using Bayesian probabilistic case linking to automatically detect infectious disease case clusters. This method aids outbreak response and prioritizes contact tracing when resources are limited.

Keywords:
COVID-19cluster detectionepidemiologyoutbreak detectionpublic health

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Area of Science:

  • Epidemiology
  • Public Health Informatics
  • Computational Biology

Background:

  • Contact tracing is crucial for infectious disease outbreak control but is resource-intensive and often manual.
  • Manual data collection and focus on individual transmission chains can obscure larger outbreak patterns and common sources.
  • Electronic health records (EHRs) offer a potential avenue for automated outbreak detection and case linkage.

Purpose of the Study:

  • To propose and explore an algorithm for identifying case clusters during infectious disease outbreaks.
  • To leverage EHR data for automated outbreak detection and case connection.
  • To assess how this approach can supplement traditional contact tracing, especially with limited resources.

Main Methods:

  • Development of a novel algorithm for case cluster identification.
  • Utilizing Bayesian probabilistic case linking to connect suspected cases.
  • Analysis of data within electronic health records systems.

Main Results:

  • The proposed algorithm can identify case clusters within a community during an outbreak.
  • This automated approach has the potential to supplement manual contact tracing efforts.
  • Demonstrated feasibility of using EHR data for outbreak analysis.

Conclusions:

  • Bayesian probabilistic case linking offers a powerful tool for enhancing infectious disease surveillance.
  • Automated case linking can improve the efficiency and effectiveness of outbreak response.
  • This methodology can help prioritize contact tracing and identify risk factors for widespread transmission.