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Methods Inf Med 2015; 54(01): 24-31
DOI: 10.3414/ME13-02-0025
Focus Theme – Original Articles
Schattauer GmbH

Bridging Data Models and Terminologies to Support Adverse Drug Event Reporting Using EHR Data[*]

G. Declerck
1   INSERM UMRS 1142, Paris, France
,
S. Hussain
1   INSERM UMRS 1142, Paris, France
,
C. Daniel
1   INSERM UMRS 1142, Paris, France
,
M. Yuksel
2   SRDC Ltd, Ankara, Turkey
,
G. B. Laleci
2   SRDC Ltd, Ankara, Turkey
,
M. Twagirumukiza
3   AGFA HealthCare, Mortsel, Belgium
,
M. -C. Jaulent
1   INSERM UMRS 1142, Paris, France
› Author Affiliations
Further Information

Publication History

received: 15 June 2013

accepted: 17 March 2014

Publication Date:
22 January 2018 (online)

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Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability 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 inter-operability 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 avail able 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 interopera bility solutions enable partial automation of the AE reporting process, which could con tribute to improve current spontaneous reporting practices and reduce under-report ing, which is currently one major obstacle in the process of acquisition of pharmacovigilance data.

Keywords

Pharmacovigilance - adverse drug event reporting - semantic interoperability - EHR data models - secondary use of EHR

* Supplementary material published on our web-site www.methods-online.com


 

  • References

  • 1 Martin RM, Kapoor KV, Wilton LV, Mann RD. Underreporting of suspected adverse drug reactions to newly marketed (“black triangle”) drugs in general practice: observational study. British Medical Journal 1998; 317 07151 119-120.
    CrossrefPubMedGoogle Scholar
  • 2 Heeley E, Riley J, Layton D, Wilton LV, Shakir SA. Prescription-event monitoring and reporting of adverse drug reactions. Lancet 2001; 358 09296 1872-1877.
    CrossrefPubMedGoogle Scholar
  • 3 Hazell L, Shakir SA. Under-reporting of adverse drug reactions: A systematic review. Drug Safety 2006; 29 (05) 385-396.
    CrossrefPubMedGoogle Scholar
  • 4 Cullen DJ, Bates DW, Small SD, Cooper JB, Nemeskal AR, Leape LL. The incident reporting system does not detect adverse drug events: A problem for quality improvement. The Joint Commission journal on quality improvement 1995; 21 (10) 541-548.
    CrossrefPubMedGoogle Scholar
  • 5 Bates DW, Evans RS, Murff H, Stetson PD, Pizziferri L, Hripcsak G. Detecting adverse events using information technology. Journal of the American Medical Informatics Association 2003; 10 (02) 115-128.
    CrossrefPubMedGoogle Scholar
  • 6 Bates DW, Spell N, Cullen DJ, Burdick E, Laird N, Petersen LA, Leape LL. The costs of adverse drug events in hospitalized patients. Journal of the American Medical Informatics Association 1997; 277 (04) 307-311.
    CrossrefPubMedGoogle Scholar
  • 7 Pirmohamed M, James S, Meakin S, Green C, Scott AK, Walley TJ, Breckenridge AM. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. British Medical Journal 2004; 329 7456 15-19.
    CrossrefPubMedGoogle Scholar
  • 8 Belton KJ, Lewis SC, Payne S, Rawlins MD, Wood SM. Attitudinal survey of adverse drug reaction reporting by medical practitioners in the United Kingdom. British journal of clinical pharmacology 1995; 39 (03) 223-226.
    CrossrefPubMedGoogle Scholar
  • 9 Hasford J, Goettler M, Munter KH, Müller-Oerlinghausen B. Physicians’ knowledge and attitudes regarding the spontaneous reporting system for adverse drug reactions. Journal of clinical epidemiology 2002; 55 (09) 945-950.
    CrossrefPubMedGoogle Scholar
  • 10 Lopez-Gonzalez E, Herdeiro MT, Figueiras A. Determinants of under-reporting of adverse drug reactions. Drug safety 2009; 32 (01) 19-31.
    CrossrefPubMedGoogle Scholar
  • 11 Linder JA, Haas JS, Iyer A, Labuzetta MA, Ibara M, Celeste M, Getty G, Bates DW. Secondary use of electronic health record data: spontaneous triggered adverse drug event reporting. Pharmacoepidemiology Drug Safety 2010; 19 (12) 1211-1215.
    PubMedGoogle Scholar
  • 12 Rozich JD, Haraden CR, Resar RK. Adverse drug event trigger tool: a practical methodology for measuring medication related harm. Quality and Safety in Health Care 2003; 12 (03) 194-200.
    CrossrefPubMedGoogle Scholar
  • 13 Brajovic S, Piazza-Hepp T, Swartz L, Dal Pan G. Quality assessment of spontaneous triggered adverse event reports received by the Food and Drug Administration. Pharmacoepidemiology Drug Safety 2012; 21 (06) 565-570.
    PubMedGoogle Scholar
  • 14 Eichelberg M, Aden T, Riesmeier J, Dogac A, Laleci GB. A survey and analysis of electronic healthcare record standards. ACM Computing Surveys (CSUR) 2005; 37 (04) 277-315.
    CrossrefPubMedGoogle Scholar
  • 15 Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shvo AS. HL7 clinical document architecture, release 2. Journal of the American Medical Informatics Association 2006; 13 (01) 30-39.
    CrossrefPubMedGoogle Scholar
  • 16 Goossen WT. Using detailed clinical models to bridge the gap between clinicians and HIT. Studies in Health Technology and Informatics 2008; 141: 3-10.
    PubMedGoogle Scholar
  • 17 ICH guideline E2B (R2), Electronic transmission of individual case safety reports - Message specification (ICH ICSR DTD Version 2.1). Final Version 2.3, Document Revision Feb. 1. 2001
    PubMedGoogle Scholar
  • 18 SALUS Project, Scalable, Standard based Interoperability Framework for Sustainable Proactive Post Market Safety Studies. http://www.salusproject.eu.
    PubMedGoogle Scholar
  • 19 Laleci GB, Yuksel M, Dogac A. Providing Semantic Interoperability between Clinical Care and Clinical Research Domains. IEEE Transactions on Information Technology in BioMedicine 2013; 17 (02) 356-369.
    PubMedGoogle Scholar
  • 20 Anil Sinaci A, Laleci Erturkmen GB. A Federated Semantic Metadata Registry Framework for Enabling Interoperability across Clinical Research and Care Domains. Journal of biomedical informatics 2013; 46 (05) 784-794.
    CrossrefPubMedGoogle Scholar
  • 21 Declerck G, Hussain S, Parès Y, Daniel C, Jaulent MC, Laleci GB. Semantic-sensitive extraction of EHR data to support adverse drug events reporting. In: Proceedings of the Semantic Web Application and Tools for Life Science Workshop. Paris, France: 2012
    Google Scholar
  • 22 Parès Y, Declerck G, Hussain S, Ng R, Jaulent MC. Building a time-saving and adaptable tool to report adverse drug events. Studies in health technology and informatics 2013; 192: 903-907.
    PubMedGoogle Scholar
  • 23 SALUS Deliverable D4.3.1: SALUS Harmonized Ontology for Post Market Safety Studies -R1. April 30, 2013. http://www.salusproject.eu/.
    PubMedGoogle Scholar
  • 24 Integrating the Healthcare Enterprise (IHE), Patient Care Coordination (PCC) Supplement - Query for Existing Data (QED). Trial Implementation Supplement, Aug. 2008
    PubMedGoogle Scholar
  • 25 Integrating the Healthcare Enterprise (IHE), Quality, Research and Public Health (QRPH) Supplement - Drug Safety Content Profile (DSC). Trial Implementation Supplement, Aug. 2010
    PubMedGoogle Scholar
  • 26 Integrating the Healthcare Enterprise (IHE), IT Infrastructure Technical Framework Supple- ment - Retrieve Form for Data Capture (RFD). Trial ImplementationSupplement, Aug. 19. 2011
    PubMedGoogle Scholar
  • 27 W3C. SKOS: Simple Knowledge Organization System. http://www.w3.org/2004/02/skos/.
    PubMedGoogle Scholar
  • 28 Declerck G, Bousquet C, Jaulent MC. Automatic generation of MedDRA terms groupings using an ontology. Studies in Health Technology and Informatics 2012; 180: 73-77.
    PubMedGoogle Scholar
  • 29 SALUS Deliverable D8.1.1: Pilot Application Scenario and Requirement Specifications of the Pilot Application. May 28, 2012. http:// www.salusproject.eu/.
    PubMedGoogle Scholar
  • 30 Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearbook of Medical Informatics 2008; 35: 128-144.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 31 Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. Journal of the American Medical Informatics Association 2009; 16 (03) 328-337.
    CrossrefPubMedGoogle Scholar
  • 32 Friedman C. Discovering novel adverse drug events using natural language processing and mining of the electronic health record. In: Artificial Intelligence in Medicine. Lecture Notes in Computer Science, 5651. Berlin Heidelberg: Springer; 2009: 1-5.
    Google Scholar