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

Christian Hoppe, Patrick Obermeier, Susann Muehlhans, Maren Alchikh, Lea Seeber, Franziska Tief, Katharina Karsch, Xi Chen, Sindy Boettcher, Sabine Diedrich, Tim Conrad, Bron Kisler and Barbara Rath ()
Additional contact information
Christian Hoppe: Charité University Medical Center Berlin
Patrick Obermeier: Charité University Medical Center Berlin
Susann Muehlhans: Charité University Medical Center Berlin
Maren Alchikh: Charité University Medical Center Berlin
Lea Seeber: Charité University Medical Center Berlin
Franziska Tief: Charité University Medical Center Berlin
Katharina Karsch: Charité University Medical Center Berlin
Xi Chen: Charité University Medical Center Berlin
Sindy Boettcher: Robert Koch Institute
Sabine Diedrich: Robert Koch Institute
Tim Conrad: Freie Universität Berlin
Bron Kisler: Vienna Vaccine Safety Initiative
Barbara Rath: Charité University Medical Center Berlin

Drug Safety, 2016, vol. 39, issue 10, No 8, 977-988

Abstract: Abstract 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.

Date: 2016
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DOI: 10.1007/s40264-016-0437-6

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