In fact, 93% of Indian respondents see the potential value of data (Globally 92%). Specifically, the Index uncovered an increase in the average amount of data managed – from 2.79 petabytes (PB) in 2016 to 6.43 PB in 2018 in India ( vis-à-vis globally: 1.45 petabytes (PB) in 2016 to 9.70PB in 2018) – and a high awareness of the value of data. The research, which surveyed 2,200 IT decision makers from both public and private organizations with 250+ employees across 18 countries and 11 industries, provides a comprehensive understanding of the state of data protection and the maturity of data protection strategies. Stud Health Technol Inform.Dell EMC announced the results of the third Global Data Protection Index revealing an increasing growth rate of data of 130% in Indian organizations (vis-a-vis Globally: 569%) and an impressive jump in data protection “adopters” of nearly 50 percentage points (48%) since 2016. Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, Suchard MA, Park RW, Wong IC, Rijnbeek PR, van der Lei J, Pratt N, Norén GN, Li YC, Stang PE, Madigan D, Ryan PB Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers.Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR.Identifying the DEAD: Development and validation of a patient-level model to predict death status in population-level claims cata. Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status. ![]() This EMA-funded project, includes data sources from eight European countries standardised to the OMOP-Common Data Model and is contracted to IQVIA as the coordinating partner. All solutions are open-source The HDS group is leading the European OHDSI Community ( The group collaborating with the European Medicines Agency (EMA) in establishing a European framework and research network for the conduct of multicentre cohort studies on the use of medicines in COVID-19 patients. Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. Its mission is to provide a new paradigm for the discovery and analysis of health data in Europe, by building a large-scale, federated network of data sources standardized to a common data model More information can be found at European Health Data and Evidence Network (EHDEN, The EHDEN project aspires to be the trusted observational research ecosystem to enable better health decisions, outcomes and care. We develop and apply natural language processing techniques to extract information from medical texts across different European languages to improve prediction models. Many health-record databases contain large amounts of unstructured, textual data. Rijnbeek, that is implementing the FAIR principles in a large federated data network. A good example is the influencial European Health Data and Evidence Network (EHDEN, project, coordinated by Dr. The group is developing open source pipelines and only performs open science, and fully endorses the Findable, Accessible, Interoperable, and Re-usable (FAIR) principles in its work. ![]() Methodological research in the field of predictive analytics, data characterisation, and causal inference is an important focus of the group. We apply advanced machine learning and statistical methods to develop and validate clinical prediction models at scale in distributed data networks. The Health Data Science group aims to develop analytical methods and tools to enable data-driven healthcare. Our group, therefore, collaborates closely with the Observational Health Data Sciences and Informatics (OHDSI) initiative ( that is responsible for the development of the OMOP-CDM, and leads its European Chapter ( to support adoption of the OMOP-CDM in Europe. A common data model and standardized analytical tools should become a de facto standard. ![]() In an ideal world, a harmonized approach would be available by which results from different databases could be combined to answer research questions. ![]() Multi-center studies are severely hampered by the fact that each database has a different database structure and uses different terminology systems.
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