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Querying EHRs with a Semantic and Entity-Oriented Query Language.

Romain Lelong1, Lina Soualmia1, Badisse Dahamna1

  • 1Department of Biomedical Informatics, Rouen University Hospital, France.

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Summary
This summary is machine-generated.

This study introduces a semantic search engine for electronic health records (EHRs), improving health information retrieval. The tool efficiently processes diverse data types for individual patients and epidemiological studies.

Keywords:
Controlled vocabularyElectronic Health RecordsInformation Storage and RetrievalSearch Engine

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Area of Science:

  • Medical Informatics
  • Health Information Management

Background:

  • Digitization of medical documents has increased, creating challenges in health information retrieval.
  • Manual retrieval from paper or computer records is time-consuming and difficult.
  • Electronic Health Records (EHRs) offer potential for improved data access.

Purpose of the Study:

  • To present the features of a novel semantic search engine implemented within EHR systems.
  • To demonstrate a tool that facilitates efficient health information retrieval.
  • To support data retrieval for both individual patient care and epidemiological research.

Main Methods:

  • Development of a flexible, scalable, and entity-oriented query language tool.
  • Implementation of a search engine capable of handling structured and unstructured data.
  • Testing queries on a database of 2,000 anonymized patient EHRs (approx. 200,000 records).

Main Results:

  • The semantic search engine accurately processed symbolic, textual, numerical, and chronological data.
  • Queries were tested from both a single-patient (caregiver) and multi-patient (epidemiology) perspective.
  • The tool demonstrated capability to support various Conceptual Data Models.

Conclusions:

  • The developed semantic search engine enhances health information retrieval from EHRs.
  • The proposed query language tool is effective for diverse data types and research needs.
  • This technology can significantly reduce the burden of manual data extraction in medical research.