Erica A. Voss, MPH
Senior Director, Observational Health Data Analytics, Global Epidemiology Organization
Janssen Research and Development
Erica Voss is currently a Senior Director in the Epidemiology Analytics group within Epidemiology at Janssen Research & Development, a Johnson & Johnson company. Starting in the IT department, she focused on data warehousing and working with large datasets. In 2007, she started working with observational datasets and later joined the Epidemiology department in 2011. Her projects typically include studying patient populations across different therapeutic areas as well as implementing OHDSI tools, such as converting the IBM MarketScan and Optum Extended claims datasets into the OMOP Common Data Model. Erica received her Bachelor of Science in Information Sciences and Technology at the Pennsylvania State University, and she received her Masters in Public Health from Johns Hopkins Bloomberg School of Public Health. She is currently pursuing her PhD in Medical Informatics at Erasmus MC. She is also a certified Project Management Professional.
Raventós B, Fernández-Bertolín S, Aragón M, et al. Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research. Clin Epidemiol. 2023;15:969-986. Published 2023 Sep 13. doi:10.2147/CLEP.S419481
Voss EA, Shoaibi A, Yin Hui Lai L, et al. Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study. EClinicalMedicine. 2023;58:101932. doi:10.1016/j.eclinm.2023.101932
Papez V, Moinat M, Voss EA, et al. Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond [published correction appears in J Am Med Inform Assoc. 2023 Apr 19;30(5):1006]. J Am Med Inform Assoc. 2022;30(1):103-111. doi:10.1093/jamia/ocac203
Voss EA, Ali SR, Singh A, Rijnbeek PR, Schuemie MJ, Fife D. Hip Fracture Risk After Treatment with Tramadol or Codeine: An Observational Study. Drug Saf. 2022;45(7):791-807. doi:10.1007/s40264-022-01198-9
Knowledge Base workgroup of the Observational Health Data Sciences and Informatics (OHDSI) collaborative. Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data. J Biomed Semantics. 2017 Mar 7;8(1):11. doi: 10.1186/s13326-017-0115-3. PubMed PMID: 28270198; PubMed
Central PMCID: PMC5341176.
Voss EA, Boyce RD, Ryan PB, van der Lei J, Rijnbeek PR, Schuemie MJ. Accuracy of an automated knowledge base for identifying drug adverse reactions. J Biomed Inform. 2017 Feb;66:72-81. doi: 10.1016/j.jbi.2016.12.005. Epub 2016 Dec 16. PubMed PMID: 27993747; PubMed Central PMCID: PMC5316295.
Voss EA, Makadia R, Matcho A, Ma Q, Knoll C, Schuemie M, DeFalco FJ, Londhe A, Zhu V, Ryan PB. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. J Am Med Inform Assoc. 2015 Feb 10. pii: ocu023. doi: 10.1093/jamia/ocu023. [Epub ahead of print] PubMed PMID: 25670757.
Voss, Erica A, Ma, Qianli, & Ryan, Patrick B. (2015). The impact of standardizing the definition of visits on the consistency of multi-database observational health research. BMC Medical Research Methodology, 15(1), 13.
Boyce RD, Ryan PB, Norén GN, Schuemie MJ, Reich C, Duke J, Tatonetti NP, Trifirò G, Harpaz R, Overhage JM, Hartzema AG, Khayter M, Voss EA, Lambert CG, Huser V, Dumontier M. Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest. Drug Saf. 2014 Jul 2. PubMed PMID: 24985530.
Voss E, Ma Q, Ryan PB. Standardization of Inpatient Visits into the ETL of OMOP Common Data Model. Observation Medical Outcomes Partnership (OMOP) – Innovation in Medical Evidence Development and Surveillance (IMEDS) 2013 Symposium. November 2013. (Best Poster Award)
Voss E, Ryan PB, Madigan D, Weiner J. Exploring Dechallenge & Rechallenge Patterns in Observational Health Care Data: Feasibility & Utility of Patient-Level Natural Experiments in Risk Identification. Observation Medical Outcomes Partnership (OMOP) 2012 Symposium. July 2012.