Zusammenfassung
Die Arbeitsgruppe Interoperabilität der Medizininformatik-Initiative (MII) ist die Plattform für die Abstimmung übergreifender Vorgehensweisen, Datenstrukturen und Schnittstellen ...zwischen den Datenintegrationszentren (DIZ) der Universitätskliniken und nationalen bzw. internationalen Interoperabilitätsgremien. Ziel ist die gemeinsame inhaltliche und technische Ausgestaltung einer über das Forschungsdatenportal für Gesundheit nutzbaren verteilten Infrastruktur zur Sekundärnutzung klinischer Versorgungsdaten. Wichtige Rahmenbedingungen sind dabei Datenschutz und IT-Sicherheit für die Nutzung von Gesundheitsdaten in der biomedizinischen Forschung. Hierfür werden in dezidierten Taskforces geeignete Methoden eingesetzt, um prozessuale, syntaktische und semantische Interoperabilität für Datennutzungsprojekte zu ermöglichen. So wurde der MII-Kerndatensatz, bestehend aus mehreren Modulen mit zugehörigen Informationsmodellen, entwickelt und mittels des Standards HL7® FHIR® implementiert, um fachliche und technische Vorgaben für die interoperable Datenbereitstellung von Versorgungsdaten durch die DIZ zu ermöglichen. Zur näheren Beschreibung dieser Datensätze dienen internationale Terminologien und konsentierte Metadaten. Die Gesamtarchitektur, einschließlich übergreifender Schnittstellen, setzt die methodischen und rechtlichen Anforderungen an eine verteilte Datennutzungsinfrastruktur z. B. durch Bereitstellung pseudonymisierter Daten oder föderierte Analysen um. Mit diesen Ergebnissen der Arbeitsgruppe Interoperabilität stellt die MII eine zukunftsweisende Lösung für den Austausch und die Nutzung von Routinedaten vor, deren Anwendbarkeit über den Zweck der Forschung hinausgeht und eine wesentliche Rolle in der digitalen Transformation des Gesundheitswesens spielen kann.
The aim of the German Medical Informatics Initiative is to establish a national infrastructure for integrating and sharing health data. To this, Data Integration Centers are set up at university ...medical centers, which address data harmonization, information security and data protection. To capture patient consent, a common informed consent template has been developed. It consists of different modules addressing permissions for using data and biosamples. On the technical level, a common digital representation of information from signed consent templates is needed. As the partners in the initiative are free to adopt different solutions for managing consent information (e.g. IHE BPPC or HL7 FHIR Consent Resources), we had to develop an interoperability layer.
First, we compiled an overview of data items required to reflect the information from the MII consent template as well as patient preferences and derived permissions. Next, we created entity-relationship diagrams to formally describe the conceptual data model underlying relevant items. We then compared this data model to conceptual models describing representations of consent information using different interoperability standards. We used the result of this comparison to derive an interoperable representation that can be mapped to common standards.
The digital representation needs to capture the following information: (1) version of the consent, (2) consent status for each module, and (3) period of validity of the status. We found that there is no generally accepted solution to represent status information in a manner interoperable with all relevant standards. Hence, we developed a pragmatic solution, comprising codes which describe combinations of modules with a basic set of status labels. We propose to maintain these codes in a public registry called ART-DECOR. We present concrete technical implementations of our approach using HL7 FHIR and IHE BPPC which are also compatible with the open-source consent management software gICS.
The proposed digital representation is (1) generic enough to capture relevant information from a wide range of consent documents and data use regulations and (2) interoperable with common technical standards. We plan to extend our model to include more fine-grained status codes and rules for automated access control.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Requirements for an IT-infrastructure in neurosciences are getting more complex.•Arising requirements as well as IT-solutions were identified and analyzed.•The role of metadata in annotating ...research data is becoming more important.•Professional IT-infrastructure involving ID-management and expert stuff is required.
The observation of growing “difficulties” in IT-infrastructures in neuroscience research during the last years led to a search for reasons and an analysis on how this phenomenon is reflected in the scientific literature. With a retrospective analysis of nine examples of multicenter research projects in the neurosciences and a literature review the observation was systematically analyzed. Results show that the rise in complexity mainly stems from two reasons: (1) more and more need for information on quality and context of research data (metadata) and (2) long-term requirements to handle the consent and identity/pseudonyms of study participants and biomaterials in relation to legal requirements. The combination of these two aspects together with very long study times and data evaluation periods are components of the subjectively perceived “difficulties”. A direct consequence of this result is that big multicenter trials are becoming part of integrated research data environments and are not standing alone for themselves anymore. This drives up the resource needs regarding the IT-infrastructure in neuroscience research. In contrast to these findings, literature on this development is scarce and the problem probably underestimated.
ABSTRACT Objectives EUReMS (European Register for Multiple Sclerosis), a project including more than ten national and regional European MS registers, is aiming to enable analyses across European ...registers by joining existing, heterogeneous MS data in four different studies. Each participating register delivered productive data comprising information on socio-demography, disease course, medical exams or treatment. In terms of data quality, especially comparability and integrity, a data handling routine has been implemented using an open source ETL (extract transform load) tool (“Talend Open Studio”) to process the large amounts of heterogeneous raw data. That approach will be presented. Approach As a first step in harmonizing datasets of different registers, a basic EUReMS data structure was defined for each of the four project studies, considering all information required to answer the research questions. Through the data handling process, the data exports are going to be converted into the prior defined study data structure to facilitate comparability and data analyses across the various registers participating in one study. In regard to quality assurance the data handling process has been validated before providing data for analyses. Results The data handling process consists of five steps: Reading/Splitting, Cleaning, Mapping and Creating Study Datasets. During the first step, data is read and split into variables that are going to be used within the study datasets. The heterogeneity of the data is again noticeable in the data types of the source files, ranging from csv or Excel to Access Database. During the cleaning step, data is checked for incorrect or missing values and are, as a way of ensuring traceability, saved in specific reject files. In the mapping step, register specific variables are mapped to the defined EUReMS denotations. By that, the heterogeneous data is harmonized, disabling misinterpretation of register specific variables, often in national language or unfamiliar abbreviations. The data is merged into study datasets that are uniform in appearance for each study and are provided to the statistical department for analyses in order to gain insight on disease related questions. Conclusion The implemented process enables the transparent, standardised and reproducible handling of heterogeneous data and is the groundwork for analyses across the various MS registers. Though it is a time-consuming task at the first implementation, we have been able to harmonise the heterogeneous data successfully.
Die Arbeitsgruppe Interoperabilität der Medizininformatik-Initiative (MII) ist die Plattform für die Abstimmung übergreifender Vorgehensweisen, Datenstrukturen und Schnittstellen zwischen den ...Datenintegrationszentren (DIZ) der Universitätskliniken und nationalen bzw. internationalen Interoperabilitätsgremien. Ziel ist die gemeinsame inhaltliche und technische Ausgestaltung einer über das Forschungsdatenportal für Gesundheit nutzbaren verteilten Infrastruktur zur Sekundärnutzung klinischer Versorgungsdaten. Wichtige Rahmenbedingungen sind dabei Datenschutz und IT-Sicherheit für die Nutzung von Gesundheitsdaten in der biomedizinischen Forschung. Hierfür werden in dezidierten Taskforces geeignete Methoden eingesetzt, um prozessuale, syntaktische und semantische Interoperabilität für Datennutzungsprojekte zu ermöglichen. So wurde der MII-Kerndatensatz, bestehend aus mehreren Modulen mit zugehörigen Informationsmodellen, entwickelt und mittels des Standards HL7® FHIR® implementiert, um fachliche und technische Vorgaben für die interoperable Datenbereitstellung von Versorgungsdaten durch die DIZ zu ermöglichen. Zur näheren Beschreibung dieser Datensätze dienen internationale Terminologien und konsentierte Metadaten. Die Gesamtarchitektur, einschließlich übergreifender Schnittstellen, setzt die methodischen und rechtlichen Anforderungen an eine verteilte Datennutzungsinfrastruktur z. B. durch Bereitstellung pseudonymisierter Daten oder föderierte Analysen um. Mit diesen Ergebnissen der Arbeitsgruppe Interoperabilität stellt die MII eine zukunftsweisende Lösung für den Austausch und die Nutzung von Routinedaten vor, deren Anwendbarkeit über den Zweck der Forschung hinausgeht und eine wesentliche Rolle in der digitalen Transformation des Gesundheitswesens spielen kann.
The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data ...integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.
Harmonizing medical data sharing frameworks is challenging. Data collection and formats follow local solutions in individual hospitals; thus, interoperability is not guaranteed. The German Medical ...Informatics Initiative (MII) aims to provide a Germany-wide, federated, large-scale data sharing network. In the last five years, numerous efforts have been successfully completed to implement the regulatory framework and software components for securely interacting with decentralized and centralized data sharing processes. 31 German university hospitals have today established local data integration centers that are connected to the central German Portal for Medical Research Data (FDPG). Here, we present milestones and associated major achievements of various MII working groups and subprojects which led to the current status. Further, we describe major obstacles and the lessons learned during its routine application in the last six months.
ABSTRACT
ObjectivesEUReMS (European Register for Multiple Sclerosis), a project including more than ten national and regional European MS registers, is aiming to enable analyses across European ...registers by joining existing, heterogeneous MS data in four different studies. Each participating register delivered productive data comprising information on socio-demography, disease course, medical exams or treatment. In terms of data quality, especially comparability and integrity, a data handling routine has been implemented using an open source ETL (extract transform load) tool (“Talend Open Studio”) to process the large amounts of heterogeneous raw data. That approach will be presented.
ApproachAs a first step in harmonizing datasets of different registers, a basic EUReMS data structure was defined for each of the four project studies, considering all information required to answer the research questions. Through the data handling process, the data exports are going to be converted into the prior defined study data structure to facilitate comparability and data analyses across the various registers participating in one study. In regard to quality assurance the data handling process has been validated before providing data for analyses.
ResultsThe data handling process consists of five steps: Reading/Splitting, Cleaning, Mapping and Creating Study Datasets. During the first step, data is read and split into variables that are going to be used within the study datasets. The heterogeneity of the data is again noticeable in the data types of the source files, ranging from csv or Excel to Access Database. During the cleaning step, data is checked for incorrect or missing values and are, as a way of ensuring traceability, saved in specific reject files. In the mapping step, register specific variables are mapped to the defined EUReMS denotations. By that, the heterogeneous data is harmonized, disabling misinterpretation of register specific variables, often in national language or unfamiliar abbreviations. The data is merged into study datasets that are uniform in appearance for each study and are provided to the statistical department for analyses in order to gain insight on disease related questions.
ConclusionThe implemented process enables the transparent, standardised and reproducible handling of heterogeneous data and is the groundwork for analyses across the various MS registers. Though it is a time-consuming task at the first implementation, we have been able to harmonise the heterogeneous data successfully.
Background:
Identification of MS registries and databases that are currently in use in Europe as well as a detailed knowledge of their content and structure is important in order to facilitate ...comprehensive analysis and comparison of data.
Methods:
National MS registries or databases were identified by literature search, from the results of the MS Barometer 2011 and by asking 33 national MS societies. A standardized questionnaire was developed and sent to the registries’ leaders, followed by telephone interviews with them.
Results:
Twenty registries were identified, with 13 completing the questionnaire and seven being interviewed by telephone. These registries differed widely for objectives, structure, collected data, and for patients and centres included. Despite this heterogeneity, common objectives of the registries were epidemiology (n=10), long-term therapy outcome (n=8), healthcare research (n=9) and support/basis for clinical trials (n=8). While physician-based outcome measures (EDSS) are used in all registries, data from patients’ perspectives were only collected in six registries.
Conclusions:
The detailed information on a large number of national MS registries in Europe is a prerequisite to facilitating harmonized integration of existing data from MS registries and databases, as well as comprehensive analyses and comparison across European populations.
Background
Multiple Sclerosis is the most common disease in young adults affecting the central nervous system. Disease may progress with acute attacks (relapsing MS) or continuously (progressive MS). ...Glucocorticosteroids are used in the treatment of acute attacks. The aim of this study was to analyse characteristics of patients with MS, and their influence on current treatment patterns of relapses with glucocorticosteroids.
Design
In 2001, the German National MS Society initiated the German MS‐Registry. Patients with relapsing MS were included (n = 5106) from this registry. Logistic regression models were used to detect trends over time. The likelihood of administration of steroids is modelled in dependence of calendar year and in dependence to confounders in treatment conditions. The sample size allows that odds ratios can be detected with a power of 90% (alpha = 0·05).
Results
Administration of glucocorticosteroids was influenced by EDSS (P < 0·0001), age (P < 0·0001) and disease duration (P < 0·0001). Therapy administration in an outpatient setting was more likely in patients with higher EDSS (P < 0·0001) and longer disease duration (P < 0·0001). The utilization of glucocorticosteroids increased with higher EDSS for all relapsing patients. Interestingly, all overutilization of glucocorticosteroids decreased over time and was accompanied by a steadily increased administration of emergent therapeutics. Although there are about 70% of registered patients with relapsing MS on immune‐modulatory treatment, almost 60% of them received glucocorticosteroids for treatment of relapses.
Conclusions
Treatment patterns with glucocorticosteroids in patients with MS are influenced mainly by EDSS and disease duration. The decline in the utilization of glucocorticosteroids is accompanied by an increase in natalizumab treatment.
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