Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM)

AbstractEfficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM’s initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements.Using a case study approach, we evaluated ODM’s ability to capture all necessary protocol elements during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health. ODM offers the advantage of a single format for institutions that deal with hundreds or thousands of concurrent clinical studies and maintain a data warehouse for these studies. For each study stage, we present a list of gaps in the ODM standard and identify necessary vendor or institutional extensions that can compensate for such gaps. The current version of ODM (1.3.2) has only partial support for study protocol and study registration data mainly because it is outside the original development goal. ODM provides comprehensive support for representation of case report forms (in both the design stage and with patient level data). Inclusion of requirements of observational, non-regulated or investigator-initiated studies (outside Food and Drug Administration (FDA) regulation) can further improve future revisions of the standard

Intégration de ressources en recherche translationnelle : une approche unificatrice en support des systèmes de santé "apprenants"

Learning health systems (LHS) are gradually emerging and propose a complimentary approach to translational research challenges by implementing close coupling of health care delivery, research and knowledge translation. To support coherent knowledge sharing, the system needs to rely on an integrated and efficient data integration platform. The framework and its theoretical foundations presented here aim at addressing this challenge. Data integration approaches are analysed in light of the requirements derived from LHS activities and data mediation emerges as the one most adapted for a LHS. The semantics of clinical data found in biomedical sources can only be fully derived by taking into account, not only information from the structural models (field X of table Y), but also terminological information (e.g. International Classification of Disease 10th revision) used to encode facts. The unified framework proposed here takes this into account. The platform has been implemented and tested in context of the TRANSFoRm endeavour, a European project funded by the European commission. It aims at developing a LHS including clinical activities in primary care. The mediation model developed for the TRANSFoRm project, the Clinical Data Integration Model, is presented and discussed. Results from TRANSFoRm use-cases are presented. They illustrate how a unified data sharing platform can support and enhance prospective research activities in context of a LHS. In the end, the unified mediation framework presented here allows sufficient expressiveness for the TRANSFoRm needs. It is flexible, modular and the CDIM mediation model supports the requirements of a primary care LHS.Les systèmes de santé "apprenants" (SSA) présentent une approche complémentaire et émergente aux problèmes de la recherche translationnelle en couplant de près les soins de santé, la recherche et le transfert de connaissances. Afin de permettre un flot d’informations cohérent et optimisé, le système doit se doter d’une plateforme intégrée de partage de données. Le travail présenté ici vise à proposer une approche de partage de données unifiée pour les SSA. Les grandes approches d’intégration de données sont analysées en fonction du SSA. La sémantique des informations cliniques disponibles dans les sources biomédicales est la résultante des connaissances des modèles structurelles des sources mais aussi des connaissances des modèles terminologiques utilisés pour coder l’information. Les mécanismes de la plateforme unifiée qui prennent en compte cette interdépendance sont décrits. La plateforme a été implémentée et testée dans le cadre du projet TRANSFoRm, un projet européen qui vise à développer un SSA. L’instanciation du modèle de médiation pour le projet TRANSFoRm, le Clinical Data Integration Model est analysée. Sont aussi présentés ici les résultats d’un des cas d’utilisation de TRANSFoRm pour supporter la recherche afin de donner un aperçu concret de l’impact de la plateforme sur le fonctionnement du SSA. Au final, la plateforme unifiée d’intégration proposée ici permet un niveau d’expressivité suffisant pour les besoins de TRANSFoRm. Le système est flexible et modulaire et le modèle de médiation CDIM couvre les besoins exprimés pour le support des activités d’un SSA comme TRANSFoRm

The Role of Free/Libre and Open Source Software in Learning Health Systems

OBJECTIVE: To give an overview of the role of Free/Libre and Open Source Software (FLOSS) in the context of secondary use of patient data to enable Learning Health Systems (LHSs). METHODS: We conducted an environmental scan of the academic and grey literature utilising the MedFLOSS database of open source systems in healthcare to inform a discussion of the role of open source in developing LHSs that reuse patient data for research and quality improvement. RESULTS: A wide range of FLOSS is identified that contributes to the information technology (IT) infrastructure of LHSs including operating systems, databases, frameworks, interoperability software, and mobile and web apps. The recent literature around the development and use of key clinical data management tools is also reviewed. CONCLUSIONS: FLOSS already plays a critical role in modern health IT infrastructure for the collection, storage, and analysis of patient data. The nature of FLOSS systems to be collaborative, modular, and modifiable may make open source approaches appropriate for building the digital infrastructure for a LHS.</p

Analysis of financial and technical feasibilty of a clinicians generated data platform of fybromyalgia syndrome patients

This master thesis analyzes the technical and economical feasibility for a medical database, based on clinically generated data of patients with the fibromyalgia syndrome. The main idea is to collect patient data on a regular basis during standard visiting hours at their doctor. Therefore it is essential to provide a data collection platform that can be simply used by the patient and doctor. The collected information (no personal data) shall be shared between researchers to enhance collaborative studies, make studies with rare diseases possible as well as to reduce the cost and effort to gather a big enough cohort group for the study. There are already several medical databases in place that collect and share patient information for research. Yet, despite the significant socioeconomic impact of fibromyalgia, no large database about this disease exists. An introduction to the fibromyalgia syndrome and its impact on society are given. Furthermore medical database technologies and medical database projects for other diseases are described. The presented technologies are further analyzed for their usefulness of creating a database to collect information about fibromyalgia syndrome patients and to use it to enhance its research. Additionally the legal requirements for maintaining such a platform as well as the potential cost are examined. Two possible business models to provide such a platform with funding are presented. Last but not least a possible use case for the collection of patient data via a survey created with REDCap and the integration process into i2b2 has been created and possible suggestions for improvements in the future have been made to bring the platform to a release ready state

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Patientenübergreifende, multiple Verwendung von Patientendaten für die klinische Forschung unter Nutzung von Archetypen

Sowohl in der Routineversorgung als auch in klinischen Studien werden immer mehr Daten elektronisch verarbeitet. Trotzdem ist ein Austausch von Daten zwischen beiden Bereichen häufig noch nicht etabliert. Dies führt dazu, dass Daten mehrfach erfasst werden müssen. Die redundante Datenerfassung ist zeitaufwändig und kann zu Inkonsistenzen zwischen Krankenhausinformationssystem (KIS) und Studiendatenmanagementsystem (SDMS) führen. Obwohl ein Datenaustausch zwischen Forschung und Versorgung oft technisch möglich wäre, scheitert er meist noch an mangelnder semantischer Interoperabilität. Archetypen sind ein innovatives Konzept zur Gestaltung von flexiblen und leicht erweiterbaren elektronischen Gesundheitsakten. Sie ermöglichen semantische Interopera-bilität zwischen Systemen, welche dieselben Archetypen nutzen. Das Archetypen-Konzept hat mittlerweile auch Eingang in internationale Standards gefunden (ISO 13606). Die openEHR-Spezifikationen definieren ein mit ISO 13606 kompatibles jedoch weiter-gehendes Modell für elektronische Gesundheitsakten. Bisher wurden Archetypen hauptsächlich für Informationssysteme in der Routineversorgung und weniger für die klinische Forschung entwickelt und genutzt. Ziel dieser Arbeit war es daher, basierend auf den openEHR-Spezifikationen und Archetypen generische Ansätze zu erarbeiten, die eine multiple Verwendung von Daten aus der Versorgung in der Forschung ermöglichen und deren Umsetzbarkeit zu prüfen. In einer Voruntersuchung wurde ermittelt, dass 35 % der in der betrachteten Studie zu erhebenden Merkmalsarten aus dem untersuchten KIS übernommen werden könnten, wenn die Daten dort elektronisch und ausreichend strukturiert vorlägen. In einem zweiten Schritt wurde mit openSDMS der Prototyp eines auf Archetypen basierenden integrierten elektronischen Gesundheitsakten- und Studiendatenmanagementsystems zur Verfügung gestellt. Aus der Voruntersuchung und der Implementierung von openSDMS wurden Anforderungen abgeleitet und eine auf openEHR-Archetypen basierende Referenzarchitektur entwickelt, welche die Nutzung von Daten aus KIS in klinischen Studien unterstützt. Dabei wird sowohl die Integration von KIS beschrieben, die auf Archetypen basieren, als auch von klassischen KIS. Kernkomponenten dieser Architektur sind auf Archetypen basierende semantische Annotationen von Studiendaten sowie Import- und Exportmodule, welche die Archetype Query Language nutzen. Die vorgestellte Referenzarchitektur ermöglicht den Übergang von der multiplen Erfassung hin zur multiplen Verwendung von Daten in Forschung und Versorgung. Um die entwickelte Referenzarchitektur realisieren zu können, werden geeignete Archetypen auch für Forschungsdaten benötigt. Daher wurden Archetypen zur Dokumentation aller Datenelemente der vier CDASH Domänen ‚Common Identifier Variables‘, ‚Common Timing Variables‘, ‚Adverse Events‘ sowie ‚Prior and Concomitant Medications‘ spezifiziert (Studiendaten). Hierzu wurden insgesamt 23 Merkmalsarten basierend auf Archetypen neu definiert, wozu drei bestehende Archetypen spezialisiert und zwei neu entwickelt wurden. Zur Definition von CDASH-konformen elektronischen Datenerhebungsbogen für die betrachteten Domänen wurden, basierend auf den spezifizierten Archetypen, vier openEHR-Templates entworfen. Ferner wurden 71 Merkmalsarten in 16 Archetypen zur Dokumentation von Studien-Metadaten definiert. Alle neu entworfenen Archetypen wurden jeweils in englischer und deutscher Sprache beschrieben und können nun als Referenzinformationsmodell für Forschungsdaten genutzt werden. Ergänzend wurden alle von den bereitgestellten Archetypen definierten Merkmalsarten auf die im Bereich der klinischen Forschung etablierten Modelle BRIDG, CDASH und ODM abgebildet

p-BioSPRE-an information and communication technology framework for transnational biomaterial sharing and access

Biobanks represent key resources for clinico-genomic research and are needed to pave the way to personalised medicine. To achieve this goal, it is crucial that scientists can securely access and share high-quality biomaterial and related data. Therefore, there is a growing interest in integrating biobanks into larger biomedical information and communication technology (ICT) infrastructures. The European project p-medicine is currently building an innovative ICT infrastructure to meet this need. This platform provides tools and services for conducting research and clinical trials in personalised medicine. In this paper, we describe one of its main components, the biobank access framework p-BioSPRE (p-medicine Biospecimen Search and Project Request Engine). This generic framework enables and simplifies access to existing biobanks, but also to offer own biomaterial collections to research communities, and to manage biobank specimens and related clinical data over the ObTiMA Trial Biomaterial Manager. p-BioSPRE takes into consideration all relevant ethical and legal standards, e.g., safeguarding donors’ personal rights and enabling biobanks to keep control over the donated material and related data. The framework thus enables secure sharing of biomaterial within open and closed research communities, while flexibly integrating related clinical and omics data. Although the development of the framework is mainly driven by user scenarios from the cancer domain, in this case, acute lymphoblastic leukaemia and Wilms tumour, it can be extended to further disease entities.FP7/2007-2013/27008