Data Integration for Future Medicine (DIFUTURE)

Abstract

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers. Objectives: The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing. Governance and Policies: Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change. Architectural Framework and Methodology: The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing. Use Cases: From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagno...

Diagnosing the Issues and Challenges in Data Integration Implementation in Public Sector

“…Policy, standard and politics [22]- [24], [26]- [31] √ √ √ √ Human resource incapability [11], [21]- [24], [27], [29], [32] √ √ √ √ Lack of governance [6], [27], [33]- [36] √ √ √ √ Expert 1 and 2 from agency A stated that the management support and organizational culture on knowledge sharing are among the challenges that the government team faced during the data integration implementation [36], [37]. Expert 3 and 4 from agency B agreed on this and described as the major obstacle are the top management buy in that will drive the implementation [38], [39].…”

“…Other than that, the poor standardization of OGD has been revealed as one of the challenges occurred. Expert 1 and 2 from agency A; and expert 3 and 4 from agency B, concurred on the idea of having a strong policy and act to bind the data integration implementation as it involves sensitive issues such as data privacy and organization's policy [36]- [39]. Other than that, Expert 1,2,3 and 4 agreed that having a standard in data integration imposed in public sector will help in cutting the cost, time and resources on the implementation [36]- [39].…”

“…Conforming to all the issues above, Expert 3 from agency B stated that the issues of lack of capable personnel is not only appears in public sector team but also the vendor team that being hired to implement the data integration project in public sector [38]. Other than that Expert 1,2 and 4 agreed that some of the IT personnel in public sector relied too much on the vendor or central agency for the data integration implementation [36], [37], [39]. This become a hurdle in maintaining the implementation across organizations.…”

Clinical Research Informatics

“…RWD studies are hampered by data protection requirements. Data integration for modern data-driven research requires the development of privacy enhancing ETL (Extract, Transfer and Load) processes [6]. The best paper from Paddock et al, describes the use of a homomorphic encryption technique to enable the analysis of patient-level data whilst it remains in an encrypted state [7].…”

Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies

“…criteria, as determined by checking the web pages of the 23 CDWs of the publications included in the study. The reference check of all included publications did not yield additional literature or documents (11,14,15,(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)…”