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A Query Engine for Self-controlled Case Series, with an application to COVID-19 EHR data.

Xiaojin Li1,2, Yan Huang1,3, Licong Cui2,3

  • 1McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|June 23, 2023
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Summary

Self-controlled case series (SCCS) research is enhanced by the new Self-Controlled Case Query (SCCQ) engine. SCCQ simplifies extracting and visualizing COVID-19 data from electronic health records for epidemiological studies.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Informatics

Background:

  • Self-controlled case series (SCCS) is a valuable epidemiological study design for within-individual comparisons.
  • Challenges exist in computational support and data extraction for SCCS methods.
  • Electronic Health Records (EHR) offer rich data but require specialized tools for cohort extraction.

Purpose of the Study:

  • To introduce a novel query engine, Self-Controlled Case Query (SCCQ), for computational support of SCCS.
  • To facilitate the extraction and visualization of self-controlled case series cohorts from large-scale EHR data.
  • To lower the technical barrier for clinical and epidemiological research using structured EHR data.

Main Methods:

  • Development of the Self-Controlled Case Query (SCCQ) engine.
  • Extraction of self-controlled case series cohorts from a large-scale COVID-19 EHR dataset.
  • Utilizing the R-Shiny visualization framework for a query result dashboard and data export.

Main Results:

  • SCCQ successfully extracts self-controlled case series cohorts from COVID-19 EHR data.
  • The R-Shiny dashboard provides visual summaries and facilitates data export in a portable format.
  • Validation experiments confirmed COVID-19 outcomes consistent with existing research.

Conclusions:

  • SCCQ provides essential computational support for SCCS, enhancing cohort exploration and data extraction.
  • The tool empowers researchers to conduct robust epidemiological studies without advanced technical skills.
  • SCCQ lowers barriers to utilizing structured EHR data for clinical and epidemiological research.