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

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Abstract
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on "Managing lnteroperability and Complexity in Health Systems". Background: SALUS project aims at building an interoperability platform and a dedicated toolkit to enable secondary use of electronic health records (EHR) data for post marketing drug surveillance. An important component of this toolkit is a drug-related adverse events (AE) reporting system designed to facilitate and accelerate the reporting process using automatic prepopulation mechanisms. Objective: To demonstrate SALUS approach for establishing syntactic and semantic interoperability for AE reporting. Method: Standard (e.g. HL7 CDA-CCD) and proprietary EHR data models are mapped to the E2B(R2) data model via SALUS Common Information Model. Terminology mapping and terminology reasoning services are designed to ensure the automatic conversion of source EHR terminologies (e.g. ICD-9-CM, ICD-10, LOINC or SNOMED-CT) to the target terminology MedDRA which is expected in AE reporting forms. A validated set of terminology mappings is used to ensure the reliability of the reasoning mechanisms. Results: The percentage of data elements of a standard E2B report that can be completed automatically has been estimated for two pilot sites. In the best scenario (i.e. the available fields in the EHR have actually been filled), only 36% (pilot site 1) and 38% (pilot site 2) of E2B data elements remain to be filled manually. In addition, most of these data elements shall not be filled in each report. Conclusion: SALUS platform's interoperability solutions enable partial automation of the AE reporting process, which could contribute to improve current spontaneous reporting practices and reduce under-reporting, which is currently one major obstacle in the process of, acquisition of pharmacovigilance data.
Keywords
semantic interoperability, secondary use of EHR, EHR data models, ELECTRONIC HEALTH RECORD, Pharmacovigilance, CLINICAL-RESEARCH, CARE, INTEROPERABILITY, DOMAINS, SYSTEM, TOOL, adverse drug event reporting

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Chicago
Declerck, G, S Hussain, C Daniel, M Yuksel, GB Laleci, Marc Twagirumukiza, and MC Jaulent. 2015. “Bridging Data Models and Terminologies to Support Adverse Drug Event Reporting Using EHR Data.” Methods of Information in Medicine 54 (1): 24–31.
APA
Declerck, G., Hussain, S., Daniel, C., Yuksel, M., Laleci, G., Twagirumukiza, M., & Jaulent, M. (2015). Bridging data models and terminologies to support adverse drug event reporting using EHR data. METHODS OF INFORMATION IN MEDICINE, 54(1), 24–31.
Vancouver
1.
Declerck G, Hussain S, Daniel C, Yuksel M, Laleci G, Twagirumukiza M, et al. Bridging data models and terminologies to support adverse drug event reporting using EHR data. METHODS OF INFORMATION IN MEDICINE. 2015;54(1):24–31.
MLA
Declerck, G, S Hussain, C Daniel, et al. “Bridging Data Models and Terminologies to Support Adverse Drug Event Reporting Using EHR Data.” METHODS OF INFORMATION IN MEDICINE 54.1 (2015): 24–31. Print.
@article{6952704,
  abstract     = {Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on {\textacutedbl}Managing lnteroperability and Complexity in Health Systems{\textacutedbl}. 
Background: SALUS project aims at building an interoperability platform and a dedicated toolkit to enable secondary use of electronic health records (EHR) data for post marketing drug surveillance. An important component of this toolkit is a drug-related adverse events (AE) reporting system designed to facilitate and accelerate the reporting process using automatic prepopulation mechanisms. 
Objective: To demonstrate SALUS approach for establishing syntactic and semantic interoperability for AE reporting. 
Method: Standard (e.g. HL7 CDA-CCD) and proprietary EHR data models are mapped to the E2B(R2) data model via SALUS Common Information Model. Terminology mapping and terminology reasoning services are designed to ensure the automatic conversion of source EHR terminologies (e.g. ICD-9-CM, ICD-10, LOINC or SNOMED-CT) to the target terminology MedDRA which is expected in AE reporting forms. A validated set of terminology mappings is used to ensure the reliability of the reasoning mechanisms. 
Results: The percentage of data elements of a standard E2B report that can be completed automatically has been estimated for two pilot sites. In the best scenario (i.e. the available fields in the EHR have actually been filled), only 36\% (pilot site 1) and 38\% (pilot site 2) of E2B data elements remain to be filled manually. In addition, most of these data elements shall not be filled in each report. 
Conclusion: SALUS platform's interoperability solutions enable partial automation of the AE reporting process, which could contribute to improve current spontaneous reporting practices and reduce under-reporting, which is currently one major obstacle in the process of, acquisition of pharmacovigilance data.},
  author       = {Declerck, G and Hussain, S and Daniel, C and Yuksel, M and Laleci, GB and Twagirumukiza, Marc and Jaulent, MC},
  issn         = {0026-1270},
  journal      = {METHODS OF INFORMATION IN MEDICINE},
  language     = {eng},
  number       = {1},
  pages        = {24--31},
  title        = {Bridging data models and terminologies to support adverse drug event reporting using EHR data},
  url          = {http://dx.doi.org/10.3414/ME13-02-0025},
  volume       = {54},
  year         = {2015},
}

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