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

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.