Innovative Digital Tools and Surveillance Systems for the Timely Detection of Adverse Events at the Point of Care: A Proof-of-Concept Study

Drug Saf. 2016 Oct;39(10):977-88. doi: 10.1007/s40264-016-0437-6.


Introduction and objective: Regulatory authorities often receive poorly structured safety reports requiring considerable effort to investigate potential adverse events post hoc. Automated question-and-answer systems may help to improve the overall quality of safety information transmitted to pharmacovigilance agencies. This paper explores the use of the VACC-Tool (ViVI Automated Case Classification Tool) 2.0, a mobile application enabling physicians to classify clinical cases according to 14 pre-defined case definitions for neuroinflammatory adverse events (NIAE) and in full compliance with data standards issued by the Clinical Data Interchange Standards Consortium.

Methods: The validation of the VACC-Tool 2.0 (beta-version) was conducted in the context of a unique quality management program for children with suspected NIAE in collaboration with the Robert Koch Institute in Berlin, Germany. The VACC-Tool was used for instant case classification and for longitudinal follow-up throughout the course of hospitalization. Results were compared to International Classification of Diseases , Tenth Revision (ICD-10) codes assigned in the emergency department (ED).

Results: From 07/2013 to 10/2014, a total of 34,368 patients were seen in the ED, and 5243 patients were hospitalized; 243 of these were admitted for suspected NIAE (mean age: 8.5 years), thus participating in the quality management program. Using the VACC-Tool in the ED, 209 cases were classified successfully, 69 % of which had been missed or miscoded in the ED reports. Longitudinal follow-up with the VACC-Tool identified additional NIAE.

Conclusion: Mobile applications are taking data standards to the point of care, enabling clinicians to ascertain potential adverse events in the ED setting and during inpatient follow-up. Compliance with Clinical Data Interchange Standards Consortium (CDISC) data standards facilitates data interoperability according to regulatory requirements.

MeSH terms

  • Adverse Drug Reaction Reporting Systems / organization & administration*
  • Algorithms
  • Child
  • Drug-Related Side Effects and Adverse Reactions / diagnosis
  • Drug-Related Side Effects and Adverse Reactions / epidemiology
  • Emergency Service, Hospital / organization & administration
  • Emergency Service, Hospital / statistics & numerical data
  • Female
  • Follow-Up Studies
  • Humans
  • Longitudinal Studies
  • Male
  • Mobile Applications*
  • Nervous System Diseases / chemically induced
  • Point-of-Care Systems / organization & administration*