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Bridging data models and terminologies to support adverse drug event reporting using EHR data.

G Declerck1, S Hussain, C Daniel

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|December 10, 2014
PubMed
Summary

The SALUS project enhances drug safety by automating adverse event (AE) reporting from electronic health records (EHR). This platform significantly reduces manual data entry, improving pharmacovigilance data collection.

Keywords:
EHR data modelsPharmacovigilanceadverse drug event reportingsecondary use of EHRsemantic interoperability

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Area of Science:

  • Health Informatics
  • Pharmacovigilance Systems
  • Health Data Interoperability

Background:

  • The SALUS project focuses on creating an interoperability platform for the secondary use of electronic health records (EHR) data in post-marketing drug surveillance.
  • A key component is an automated adverse event (AE) reporting system to streamline data submission.

Purpose of the Study:

  • To demonstrate the SALUS approach for achieving syntactic and semantic interoperability in AE reporting.
  • To facilitate the secondary use of EHR data for drug safety monitoring.

Main Methods:

  • Mapping standard and proprietary EHR data models to the E2B(R2) data model using the SALUS Common Information Model.
  • Implementing terminology mapping and reasoning services for automatic conversion of EHR terminologies (e.g., ICD-9-CM, LOINC) to MedDRA for AE reporting.
  • Utilizing a validated set of terminology mappings to ensure the reliability of automated conversions.

Main Results:

  • Automated data entry for AE reports was evaluated at two pilot sites.
  • In optimal conditions, 36% (pilot site 1) and 38% (pilot site 2) of E2B data elements required manual completion.
  • A significant portion of these remaining elements do not need to be filled for every report.

Conclusions:

  • The SALUS platform's interoperability solutions partially automate the AE reporting process.
  • This automation has the potential to enhance spontaneous reporting practices.
  • Reducing manual data entry can help mitigate under-reporting, a major challenge in pharmacovigilance data acquisition.