Systematizing FAIR research data management in biomedical research projects: a data life cycle approach

Biomedical researchers are facing data management challenges brought by a new generation of data driven by the advent of translational medicine research. These challenges are further complicated by the recent calls for data re-use and long-term stewardship spearheaded by the FAIR principles initiative. As a result, there is an increasingly wide-spread recognition that advancing biomedical science is becoming dependent on the application of data science to manage and utilize highly diverse and complex data in ways that give it context, meaning, and longevity beyond its initial purpose. However, current methods and practices in biomedical informatics remain to adopt a traditional linear view of the informatics process (collect, store and analyse); focusing primarily on the challenges in data integration and analysis, which are challenges only pertaining to a part of the overall life cycle of research data. The aim of this research is to facilitate the adoption and integration of data management practices into the research life cycle of biomedical projects, thus improving their capabilities into solving data management-related challenges that they face throughout the course of their research work. To achieve this aim, this thesis takes a data life cycle approach to define and develop a systematic methodology and framework towards the systematization of FAIR data management in biomedical research projects. The overarching contribution of this research is the provision of a data-state life cycle model for research data management in Biomedical Translational Research Projects. This model provides insight into the dynamics between 1) the purpose of a research-driven data use case, 2) the data requirements that renders data in a state fit for purpose, 3) the data management functions that prepare and act upon data and 4) the resulting state of data that is _t to serve the use case. This insight led to the development of a FAIR data management framework, which is another contribution of this thesis. This framework provides data managers the groundwork, including the data models, resources and capabilities, needed to build a FAIR data management environment to manage data during the operational stages of a biomedical research project. An exemplary implementation of this architecture (PlatformTM) was developed and validated by real-world research datasets produced by collaborative research programs funded by the Innovative Medicine Initiative (IMI) BioVacSafe 1 , eTRIKS 2 and FAIRplus 3.Open Acces

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 application of medical terminologies to free-text in routine databases using the example of strategies to reduce infant mortality

Hintergrund Die Säuglingssterblichkeitsrate (IMR), ein wichtiger Indikator für die Qualität eines Gesundheitssystems, liegt in Deutschland seit 10 Jahren bei rund 3.5‰. Generische Qualitätsindikatoren (QIs), wie sie seit 2010 in Deutschland verwendet werden, tragen wesentlich zu einem so guten Wert bei, scheinen aber nicht in der Lage zu sein, den IMR weiter zu reduzieren. Die neonatale Sterblichkeitsrate (NMR) trägt zu 65-70% der IMR bei. Der vorgestellte Ansatz schlägt daher eine Einzelfallanalyse neonataler Todesfälle auf der Grundlage von Krankenakten vor. Die meisten elektronischen Krankenakten enthalten noch immer große Mengen an Freitextdaten. Die semantische Auswertung solcher Daten erfordert, dass die Daten mit ausreichenden Klassifizierungen kodiert oder in eine wissensbasierte Datenbank umgewandelt werden. Methodik Die Nordic-Baltic-Classification (NBC) wurde zur Erkennung vermeidbarer neonataler Todesfälle verwendet. Diese Klassifikation wurde auf eine Stichprobe von 1.968 neonatalen Todesfällen angewandt, die über 90% aller neonatalen Todesfälle in Ost-Berlin von 1973 bis 1989 darstellen. Alle Fälle wurden damals von einer speziellen Kommission verschiedener Experten auf der Grundlage der vollständigen perinatalen und klinischen Daten auf ihre Vermeidbarkeit hin analysiert. Der entwickelte Ansatz ermöglicht es, Datenbanken, die über SQL (Structured Query Language) zugänglich sind, direkt über semantische Abfragen zu durchsuchen, ohne dass weitere Transformationen erforderlich sind. Dazu wurden 1.) eine Erweiterung von SQL „Ontology-SQL“ (O-SQL) entwickelt, die es ermöglicht, semantische Ausdrücke zu verwenden, 2.) ein Framework entwickelt, das einen Standardterminologieserver verwendet, um Freitext enthaltende Datenbanktabellen zu annotieren und 3.) ein Parser entwickelt, der O-SQL Ausdrücke in SQL konvertiert, so dass semantische Abfragen direkt an den Datenbankserver weitergeleitet werden können. Ergebnisse Die NBC wurde verwendet, um die Gruppe der Fälle auszuwählen, die ein hohes Vermeidungspotenzial hatten. Die ausgewählte Gruppe stellte 6,0% aller Fälle dar und 60,4% der Fälle innerhalb dieser Gruppe wurden tatsächlich als vermeidbar oder bedingt vermeidbar beurteilt. Die automatische Erkennung von Fehlbildungen ergab einen F1-Wert von 0,94. Darüber hinaus wurde die Verallgemeinerbarkeit des Ansatzes mit verschiedenen semantischen Abfragen nachgewiesen und dessen Güte mit F1-Werten von 0,91 bis 0,98 gemessen. Zusammenfassung Die Ergebnisse zeigen, dass die vorgestellte Methode automatisch anwendbar ist und ein leistungsfähiges und hochsensitives und -spezifisches Werkzeug zur Auswahl potenziell vermeidbarer neonataler Todesfälle und damit zur Unterstützung einer effizienten Einzelfallanalyse darstellt. Die nahtlose Verknüpfung von Ontologien und Standardtechnologien aus dem Datenbankbereich stellt einen wichtigen Bestandteil der unstrukturierten Datenanalyse dar. Die entwickelte Technologie lässt sich problemlos auf aktuelle Daten anwenden und unterstützt das immer wichtiger werdende Feld der translationalen Forschung.Background The infant mortality rate (IMR), a key indicator of the quality of a healthcare system, has remained at approximately 3.5‰ for the past 10 years in Germany. Generic quality indicators (QIs), as used in Germany since 2010, greatly help to ensure such a good value but do not seem to be able to further reduce the IMR. The neonatal mortality rate (NMR) contributes to 65-70% of the IMR. The presented approach therefore proposes single-case analysis of neonatal deaths on base of medical records. Most electronic medical records still contain large amounts of free-text data. Semantic evaluation of such data requires the data to be encoded with sufficient classifications or transformed into a knowledge-based database. Methods The Nordic-Baltic classification (NBC) was used to detect avoidable neonatal deaths. This classification has been applied to a sample of 1,968 neonatal death records, which represent over 90% of all neonatal deaths in East Berlin from 1973 to 1989. All cases were analyzed as to their preventability based on the complete perinatal and clinical data by a special commission of different experts. The developed approach allows databases accessible via SQL (Structured Query Language) to be searched directly through semantic queries without the need for further transformations. Therefore, I) an extension to SQL named Ontology-SQL (O-SQL) that allows to use semantic expressions, II) a framework that uses a standard terminology server to annotate free-text containing database tables and III) a parser that rewrites O-SQL to SQL, so that such queries can be passed to the database server, have been developed. Results The NBC was used to select the group of cases that had a high potential of avoidance. The selected group represented 6.0% of all cases, and 60.4% of the cases within that group were judged avoidable or conditionally avoidable. The automatic detection of malformations showed an F1 score of 0.94. Furthermore, the generability has been proved with different semantic queries and was measured with between 0.91 and 0.98. Conclusion The results show, that the presented method can be applied automatically and is a powerful and highly specific tool for selecting potentially avoidable neonatal deaths and thus for supporting efficient single case analysis. The seamless connection of ontologies and standard technologies from the database field represents an important constituent of unstructured data analysis. The developed technology can be readily applied to current data and supports the increasingly important field of translational research

From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome

The connectome is regarded as the key to brain function in health and disease. Structural and functional neuroimaging enables us to measure brain connectivity in the living human brain. The field of connectomics describes the connectome as a mathematical graph with its connection strengths being represented by connectivity matrices. Graph theory algorithms are used to assess the integrity of the graph as a whole and to reveal brain network biomarkers for brain diseases; however, the faulty wiring of single connections or subnetworks as the structural correlate for neurological or mental diseases remains elusive. We describe a novel approach to represent the knowledge of human brain connectivity by a semantic network – a formalism frequently used in knowledge management to describe the semantic relations between objects. In our novel approach, objects are brain areas and connectivity is modeled as semantic relations among them. The semantic network turns the graph of the connectome into an explicit knowledge base about which brain areas are interconnected. Moreover, this approach can semantically enrich the measured connectivity of an individual subject by the semantic context from ontologies, brain atlases and molecular biological databases. Integrating all measurements and facts into one unified feature space enables cross-modal comparisons and analyses. We used a query mechanism for semantic networks to extract functional, structural and transcriptome networks. We found that in general higher structural and functional connectivity go along with a lower differential gene expression among connected brain areas; however, subcortical motor areas and limbic structures turned out to have a localized high differential gene expression while being strongly connected. In an additional explorative use case, we could show a localized high availability of fkbp5, gmeb1, and gmeb2 genes at a connection hub of temporo-limbic brain networks. Fkbp5 is known for having a role in stress-related psychiatric disorders, while gmeb1 and gmeb2 encode for modulator proteins of the glucocorticoid receptor, a key receptor in the hormonal stress system. Semantic networks tremendously ease working with multimodal neuroimaging and neurogenetics data and may reveal relevant coincidences between transcriptome and connectome networks

A standards-based ICT framework to enable a service-oriented approach to clinical decision support

This research provides evidence that standards based Clinical Decision Support (CDS) at the point of care is an essential ingredient of electronic healthcare service delivery. A Service Oriented Architecture (SOA) based solution is explored, that serves as a task management system to coordinate complex distributed and disparate IT systems, processes and resources (human and computer) to provide standards based CDS. This research offers a solution to the challenges in implementing computerised CDS such as integration with heterogeneous legacy systems. Reuse of components and services to reduce costs and save time. The benefits of a sharable CDS service that can be reused by different healthcare practitioners to provide collaborative patient care is demonstrated. This solution provides orchestration among different services by extracting data from sources like patient databases, clinical knowledge bases and evidence-based clinical guidelines (CGs) in order to facilitate multiple CDS requests coming from different healthcare settings. This architecture aims to aid users at different levels of Healthcare Delivery Organizations (HCOs) to maintain a CDS repository, along with monitoring and managing services, thus enabling transparency. The research employs the Design Science research methodology (DSRM) combined with The Open Group Architecture Framework (TOGAF), an open source group initiative for Enterprise Architecture Framework (EAF). DSRM’s iterative capability addresses the rapidly evolving nature of workflows in healthcare. This SOA based solution uses standards-based open source technologies and platforms, the latest healthcare standards by HL7 and OMG, Decision Support Service (DSS) and Retrieve, Update Locate Service (RLUS) standard. Combining business process management (BPM) technologies, business rules with SOA ensures the HCO’s capability to manage its processes. This architectural solution is evaluated by successfully implementing evidence based CGs at the point of care in areas such as; a) Diagnostics (Chronic Obstructive Disease), b) Urgent Referral (Lung Cancer), c) Genome testing and integration with CDS in screening (Lynch’s syndrome). In addition to medical care, the CDS solution can benefit organizational processes for collaborative care delivery by connecting patients, physicians and other associated members. This framework facilitates integration of different types of CDS ideal for the different healthcare processes, enabling sharable CDS capabilities within and across organizations

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe