Christian Hoppe1,2, Patrick Obermeier1,2, Susann Muehlhans1,2, Maren Alchikh1,2, Lea Seeber1,2, Franziska Tief1,2, Katharina Karsch1,2, Xi Chen1,2, Sindy Boettcher3, Sabine Diedrich3, Tim Conrad4, Bron Kisler2,5, Barbara Rath6,7. 1. Department of Pediatrics, Charité University Medical Center Berlin, Berlin, Germany. 2. Vienna Vaccine Safety Initiative, Berlin, Germany. 3. National Reference Centre for Poliomyelitis and Enteroviruses, Robert Koch Institute, Berlin, Germany. 4. Department of Mathematics and Computer Sciences, Freie Universität Berlin, Berlin, Germany. 5. Clinical Data Interchange Standards Consortium, Austin, TX, USA. 6. Department of Pediatrics, Charité University Medical Center Berlin, Berlin, Germany. barbara.rath@gmail.com. 7. Vienna Vaccine Safety Initiative, Berlin, Germany. barbara.rath@gmail.com.
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.
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.
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Authors: Michael M McNeil; Julianne Gee; Eric S Weintraub; Edward A Belongia; Grace M Lee; Jason M Glanz; James D Nordin; Nicola P Klein; Roger Baxter; Allison L Naleway; Lisa A Jackson; Saad B Omer; Steven J Jacobsen; Frank DeStefano Journal: Vaccine Date: 2014-08-06 Impact factor: 3.641
Authors: Barbara Rath; Tim Conrad; Puja Myles; Maren Alchikh; Xiaolin Ma; Christian Hoppe; Franziska Tief; Xi Chen; Patrick Obermeier; Bron Kisler; Brunhilde Schweiger Journal: Expert Rev Anti Infect Ther Date: 2017-05-12 Impact factor: 5.091