Comparative case study
Data Extraction And Management In Networks Of Observational Health Care Databases For Scientific Research: A Comparison Among EU-ADR, OMOP, Mini-Sentinel And MATRICE Strategies
Authors:
Rosa Gini ,
Agenzia regionale di sanità della Toscana, Florence, Italy; and Erasmus MC University Medical Center, Department of Medical Informatics, Rotterdam, Netherlands
Martijn Schuemie,
Janssen Research & Development, Epidemiology, Titusville, New Jersey, United States; and Observational Health Data Sciences and Informatics (OHDSI) New York, New York, United States
Jeffrey Brown,
Harvard Medical School, Department of Population Medicine, Boston, Massachusetts, United States
Patrick Ryan,
Janssen Research & Development, Epidemiology, Titusville, New Jersey, United States; and Observational Health Data Sciences and Informatics (OHDSI) New York, New York, United States
Edoardo Vacchi,
Università degli Studi di Milano, Dipartimento di Informatica, Milan, Italy
Massimo Coppola,
Consiglio Nazionale delle Ricerche, Istituto di Scienza e Tecnologie dell'Informazione, Pisa, Italy
Walter Cazzola,
Università degli Studi di Milano, Dipartimento di Informatica, Milan, Italy
Preciosa Coloma,
Erasmus MC University Medical Center, Department of Medical Informatics, Rotterdam, Netherlands
Roberto Berni,
Agenzia regionale di sanità della Toscana, Florence, Italy
Gayo Diallo,
Université Bordeaux, LESIM - ISPED, Bordeaux, France
José Luis Oliveira,
University of Aveiro, DETI/IEETA, Aveiro, Portugal
Paul Avillach,
Harvard Medical School, Center for Biomedical Informatics, Boston, Massachusetts, United States
Gianluca Trifirò,
Erasmus University Medical Center, Department of Medical Informatics, Rotterdam, Netherlands
Peter Rijnbeek,
Erasmus University Medical Center, Department of Medical Informatics, Rotterdam, Netherlands
Mariadonata Bellentani,
Agenzia nazionale per i servizi sanitari regionali, Rome, Italy
Johan van Der Lei,
Erasmus University Medical Center, Department of Medical Informatics, Rotterdam, Netherlands
Niek Klazinga,
University of Amsterdam, Academic Medical Center, Amsterdam, Netherlands
Miriam Sturkenboom
Erasmus University Medical Center, Department of Medical Informatics, Rotterdam, Netherlands
Abstract
Introduction: We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can’t be shared, requiring a distributed network approach where data processing is performed prior to data sharing.
Case Descriptions and Variation Among Sites: We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge(EU-ADR),Observational Medical Outcomes Partnership(OMOP), the Food and Drug Administration’s (FDA’s) Mini-Sentinel, and the Italian network—the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE).
Findings: National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++.
Conclusion: Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases.
How to Cite:
Gini R, Schuemie M, Brown J, Ryan P, Vacchi E, Coppola M, et al.. Data Extraction And Management In Networks Of Observational Health Care Databases For Scientific Research: A Comparison Among EU-ADR, OMOP, Mini-Sentinel And MATRICE Strategies. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2016;4(1):2. DOI: http://doi.org/10.13063/2327-9214.1189
Published on
02 Aug 2016.
Peer Reviewed
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