MIRACUM: Medical Informatics in Research and Care in University Medicine

Abstract

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. Objectives: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. Governance and Policies: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. Architectural Framework and Methodology: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. Use Cases: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. Results: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. Discussion: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.

KETOS: Clinical decision support and machine learning as a service – A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services

“…The feasibility of this approach has been demonstrated by implementing a prototype using the architecture illustrated in Fig 2. To provide patient data in a standardized manner we used the OMOP common data model, which has been chosen by the OHDSI project [16], the European EHDEN project [17], and the MIRACUM consortium [18]. This data source is augmented with the widely used FHIR web service standard for patient data to make data available to researchers and end-user applications.…”

Mapping the Entire Record—An Alternative Approach to Data Access from Medical Logic Modules

“…[35][36][37] UKER is part of one of the research consortia within this project, termed Medical Informatics in Research and Care in University Medicine (MIRACUM). 38 The hospitals within the consortia are currently building data integration centers to enable patient cohort identification for clinical trials across institutions. Currently, we investigate the suitability of MLMs as sharable patient cohort identification algorithms within MIRACUM.…”

Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC

“…21 Both applications are part of the Samply software environment, 22 for which expertise was available within the BBMRI-ERIC community and which is also used in other projects. 11,[23][24][25] It allows for manual data entry via the CRFs to enable the participation of those biobanks, which are only able to provide data manually, but also provides an application programmable interface (API) for the automatic import of XML-encoded data.…”