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Background: In Germany, around 2.250 children and adolescents are diagnosed with cancer each year. Despite generally positive long-term survival rates, many patients must cope with late effects of the disease and its treatment. This highlights the need for a well-structured, long-term approach addressing both physical and mental health issues. Currently, the German healthcare system lacks such comprehensive structures. Our study aims to evaluate the effectiveness of a structured, multidisciplinary long-term approach compared to conventional "treatment as usual" (TAU)., Methods: A prospective, multicenter study with ten pediatric university clinics in Germany will be conducted. The cluster-randomization takes place at the clinic level. Children and adolescents who completed their cancer treatment at least five years ago and their parents will be eligible to participate. While the control group (CG) receives TAU, the intervention group (IG) participates in a structured program. This program includes risk-based medical treatment and psychosocial interventions tailored to each patient's individual needs within a two-month timeframe. The primary outcome is the improvement of self-efficacy. Secondary outcomes are satisfaction with health care, improvement of health-related quality of life (HRQoL), reduction of mental health problems, and improvement of transition readiness., Discussion: This approach has the potential to optimize the health care for individuals who survived cancer during childhood or adolescence. It addresses the challenges of overuse, underuse, and misuse of health care resources. By considering both medical and psychosocial factors and promoting increased self-efficacy, independent from parental involvement, it may facilitate a smoother transition to adult medicine and enhance adherence to lifelong aftercare. If proven successful, this approach will contribute to the integration of multidisciplinary strategies into standard healthcare practice., Trial Registration: German Clinical Trials Register DRKS00029269. Registered on December 23, 2022., (© 2024. The Author(s).)

Introduction: The reuse of clinical data from clinical routine is a topic of research within the field of medical informatics under the term secondary use. In order to ensure the correct use and interpretation of data, there is a need for context information of data collection and a general understanding of the data. The use of metadata as an effective method of defining and maintaining context is well-established, particularly in the field of clinical trials. The objectives of this paper is to examine a method for integrating routine clinical data using metadata., Methods: To this end, clinical forms extracted from a hospital information system will be converted into the FHIR format. A particular focus is placed on the consistent use of a metadata repository (MDR)., Results: A metadata-based approach using an MDR system was developed to simplify data integration and mapping of structured forms into FHIR resources, while offering many advantages in terms of flexibility and data quality. This facilitated the management and configuration of logic and definitions in one place, enabling the reusability and secondary use of data., Discussion: This work allows the transfer of data elements without loss of detail and simplifies integration with target formats. The approach is adaptable for other ETL processes and eliminates the need for formatting concerns in the target profile.

Introduction: Conducting clinical studies is an integral part of the clinical research repertoire of university hospitals. A wealth of organizational competences must always be available in a central location and in an up-to-date form for appropriate administration. Information such as the number of ongoing studies, and the number of enrolled participants is required for tasks related to e.g. sponsor quality management and KPIs. A registry for clinical trials can answer these questions and enhance the exchange of information., Methods: Requirements for an in-house registry for clinical trials were defined in a multidisciplinary task force. The requirements included interfaces and key abilities to create customized reports to fulfill the obligation to provide evidence., Results: The study registry is productive since May 2020 and internal interfaces have been implemented to ensure consistency between systems and the documented studies. Manually recorded data is enhanced by interfaces to primary registers. The comprehensive data set in the study register enables the creation of individual queries at any time for a variety of questions., Discussion: The UKSH study register has already demonstrated its usefulness in various applications and several projects. The extensive data set and the modular realization allows many current and future requirements to be met.

This paper explores the challenges and lessons learned during the mapping of HL7 v2 messages structured using custom schema to openEHR for the Medical Data Integration Center (MeDIC) of the University Hospital, Schleswig-Holstein (UKSH). Missing timestamps in observations, missing units of measurement, inconsistencies in decimal separators and unexpected datatypes were identified as critical inconsistencies in this process. These anomalies highlight the difficulty of automating the transformation of HL7 v2 data to any standard, particularly openEHR, using off-the-shelf tools. Addressing these anomalies is crucial for enhancing data interoperability, supporting evidence-based research, and optimizing clinical decision-making. Implementing proper data quality measures and governance will unlock the potential of integrated clinical data, empowering clinicians and researchers and fostering a robust healthcare ecosystem.

Innovation in cancer therapy has increased childhood cancer survival rates. However, survivors are still at risk of developing late effects. In the digital transformation of the health sector, the Survivorship Passport (SurPass) can support long-term follow-up care plans. Gaps in seamless connectivity among hospital departments, primary care, combined with the time of health professionals required to collect and fill-in health data in SurPass, are barriers to its adoption in daily clinical practice. The PanCareSurPass (PCSP) project was motivated to address these gaps by a new version of SurPass (v2.0) that supports semi-automatic assembly from organizational Electronic Health Record (EHR) systems of the treatment summary data using HL7 FHIR, to create SurPass, and to link it to regional or national digital health infrastructures in six European countries. In this paper we present the methodology used to develop the SurPass technical implementation strategy with special focus on the European Health Data Space (EHDS). The recently provisionally approved EHDS regulation instruments a digital health data ecosystem with opportunities for cost-effective SurPass implementation across Europe. Moving forward, a European HL7 FHIR SurPass Implementation Guide along with synthetic data sets, and validation tools can enrich the European Electronic Health Record Exchange Format (EEHRxF) with use cases on health & wellness of childhood cancer survivors.

The integration of tumor-related diagnosis and therapy data is a key factor for cancer-related collaborative projects and research projects on-site. The Medical Data Integration Center (MeDIC) of the University Hospital Schleswig-Holstein, resulting from the Medical Informatics Initiative and Network University Medicine in Germany, has agreed on an openEHR-based data management based on a centralized repository with harmonized annotated data. Consequently, the oncological data should be integrated into the MeDIC to interconnect the information and thus gain added value. A uniform national data set for tumor-related reports is already defined for the cancer registries. Therefore, this work aims to transform the national oncological basis data set for tumor documentation (oBDS) so that it can be stored and utilized properly in the openEHR repository of the MeDIC. In a previous work openEHR templates representing the oncological basis data set were modeled. These templates were used to implement a processing pipeline including a metadata repository, which defines the mappings between the elements, a FHIR terminology service for annotation and validation, resulting in a tool to automatically build openEHR compositions from oBDS data. The prototype proved the feasibility of the referred mapping, integration into the MeDIC is straightforward and the architecture introduced is adaptable to future needs by design.

Importance: There is a lack of trials examining the effect of counseling interventions for child, adolescent, and younger adult (CAYA) cancer survivors., Objective: To assess lifestyle habits and the psychosocial situation of CAYAs to determine the efficacy of needs-based interventions in the CARE for CAYA program (CFC-P)., Design, Setting, and Participants: The CFC-P was conducted as a multicenter program in 14 German outpatient clinics, mainly university cancer centers. Recruitment began January 1, 2018; a randomized clinical trial was conducted until July 15, 2019; and intervention was continued as a longitudinal cohort study until March 31, 2021. Data preparation was conducted from April 1, 2021, and analysis was conducted from August 14, 2021, to May 31, 2022. Herein, predefined confirmatory analyses pertain to the RCT and descriptive results relate to the overall longitudinal study. Data analysis was based on the full analysis set, which is as close as possible to the intention-to-treat principle., Intervention: A comprehensive assessment determined needs in physical activity, nutrition and psychooncology. Those with high needs participated in 1 to 3 modules. In the RCT, the IG received 5 counseling sessions plus newsletters, while the control group CG received 1 counseling session., Main Outcomes and Measures: The primary outcome was the change in the rate of CAYAs with high needs at 52 weeks. Secondary outcomes were feasibility, modular-specific end points, satisfaction, quality of life, and fatigue., Results: Of 1502 approached CAYAs aged 15 to 39 years, 692 declined participation. Another 22 CAYAs were excluded, resulting in 788 participants. In the randomized clinical trial, 359 CAYAs were randomized (intervention group [IG], n = 183; control group [CG], n = 176), and 274 were followed up. In the RCT, the median age was 25.0 (IQR, 19.9-32.2) years; 226 were female (63.0%) and 133 male (37.0%). After 52 weeks, 120 CAYAs (87.0%) in the IG and 115 (86.5%) in the CG still had a high need in at least 1 module (odds ratio, 1.04; 95% CI, 0.51-2.11; P = .91). Both groups reported reduced needs, improved quality of life, reduced fatigue, and high satisfaction with the CFC-P., Conclusions and Relevance: In this randomized clinical trial, the implementation of a lifestyle program in this cohort was deemed necessary, despite not meeting the primary outcome. The interventions did not alter the rate of high needs. The results may provide guidance for the development of multimodal interventions in the follow-up care of CAYAs., Trial Registration: German Clinical Trial Register: DRKS00012504.

Competing Interests: Disclosure The authors report no conflicts of interest in this work.

Purpose: Childhood cancer survivors (CCS) are at risk for increased morbidity and reduced quality of life associated with treatment-related late effects. In Germany, however, only a few of the more than 40,000 CCS registered in the German Childhood Cancer Registry (GCCR) currently benefit from adequate clinical long-term follow-up (LTFU) structures. To establish a comprehensive knowledge base on CCS' long-term health in Germany, a database was developed in cooperation with the GCCR. Following a first evaluation phase at two German university centres, this database will be implemented more widely within Germany allowing longitudinal documentation of clinical LTFU data., Methods: The feasibility study cohort comprised 208 CCS aged 18 or older whose medical, mental and psychosocial health data were collected during routine LTFU or first clinic visits in adult care. CCS were enrolled from 04/2021 to 12/2022, and data entry was completed by 03/2023. Descriptive data analysis was conducted. All CCS were stratified into three risk groups (RG) based on their individual risk for developing late effects resulting from their respective diagnoses and treatments., Results: Chronic health conditions of various organ systems associated with late and long-term effects of cancer therapy affected CCS in all RG supporting the clinical relevance of risk-adapted LTFU. Enrolment into the database was feasible and broadly accepted amongst CCS., Conclusion: Implementation of a clinical follow-up care infrastructure and database in Germany will pave the way to collect clinically evaluated and regularly updated health data of potentially over 40,000 German CCS and facilitate future national and international cooperation., (© 2023. The Author(s).)

To provide clinical data in distributed research architectures, a fundamental challenge involves defining and distributing suitable metadata within Metadata Repositories. Especially for structured data, data elements need to be bound against suitable terminologies; otherwise, other systems will only be able to interpret the data with complex and error-prone manual involvement. As current Metadata Repository implementations lack support for querying externally defined terminologies in FHIR terminology servers, we propose an intermediate solution that uses appropriate annotations on metadata elements to allow run-time Terminology Services mediated queries of that metadata. This allows a very clear separation of concerns between the two related systems, greatly simplifying terminological maintenance. The system performed well in a prototypical deployment.

The large variability of data models, specifications, and interpretations of data elements is particular to the healthcare domain. Achieving semantic interoperability is the first step to enable reuse of healthcare data. To ensure interoperability, metadata repositories (MDR) are increasingly used to manage data elements on a structural level, while terminology servers (TS) manage the ontologies, terminologies, coding systems and value sets on a semantic level. In practice, however, this strict separation is not always followed; instead, semantical information is stored and maintained directly in the MDR, as a link between both systems is missing. This may be reasonable up to a certain level of complexity, but it quickly reaches its limitations with increasing complexity. The goal of this approach is to combine both components in a compatible manner. We present TermiCron, a synchronization engine that provides synchronized value sets from TS in MDRs, including versioning and annotations. Prototypical results were shown for the terminology server Ontoserver and two established MDR systems. Bridging the semantic and structural gap between the two infrastructure components, this approach enables shared use of metadata and reuse of corresponding health information by establishing a clear separation of the two systems and thus serves to strengthen reuse as well as to increase quality.

While HL7 FHIR and its terminology package have seen a rapid uptake by the research community, in no small part due to the wide availability of tooling and resources, there are some areas where tool availability is still lacking. In particular, the comparison of terminological resources, which supports the work of terminologists and implementers alike, has not yet been sufficiently addressed. Hence, we present TerminoDiff, an application to semantically compare FHIR R4 CodeSystem resources. Our tool considers differences across all levels required, i.e. metadata and concept differences, as well as differences in the edge graph, and surfaces them in a visually digestible fashion.

Background: Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term "metadata" and its use is not always unambiguous., Objective: This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse., Methods: A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used., Results: The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas., Conclusions: Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context., (©Hannes Ulrich, Ann-Kristin Kock-Schoppenhauer, Noemi Deppenwiese, Robert Gött, Jori Kern, Martin Lablans, Raphael W Majeed, Mark R Stöhr, Jürgen Stausberg, Julian Varghese, Martin Dugas, Josef Ingenerf. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.01.2022.)

With the steady increase in the connectivity of the healthcare system, new requirements and challenges are emerging. In addition to the seamless exchange of data between service providers on a national level, the local legacy data must also meet the new requirements. For this purpose, the applications used must be tested securely and sufficiently. However, the availability of suitable and realistic test data is not always given. Therefore, this study deals with the creation of test data based on real electronic health record data provided by the Medical Information Mart for Intensive Care (MIMIC-IV) database. In addition to converting the data to the current FHIR R4, conversion to the core data sets of the German Medical Informatics Initiative was also presented and made available. The test data was generated to simulate a legacy data transfer. Moreover, four different FHIR servers were tested for performance. This study is the first step toward comparable test scenarios around shared datasets and promotes comparability among providers on a national level.

To ensure semantic interoperability within healthcare systems, using common, curated terminological systems to identify relevant concepts is of fundamental importance. The HL7 FHIR standard specifies means of modelling terminological systems and appropriate ways of accessing and querying these artefacts within a terminology server. Hence, initiatives towards healthcare interoperability like IHE specify not only software interfaces, but also common codes in the form of value sets and code systems. The way in which these coding tables are provided is not necessarily compatible to the current version of the HL7 FHIR specification and therefore cannot be used with current HL7 FHIR-based terminology servers. This work demonstrates a conversion of terminological resources specified by the Integrating the Healthcare Initiative in the ART-DECOR platform, partly available in HL7 FHIR, to ensure that they can be used within a HL7 FHIR-based terminological server. The approach itself can be used for other terminological resources specified within ART-DECOR but can also be used as the basis for other code-driven conversions of proprietary coding schemes.

The primary intention of any scientific work is to share the gained knowledge and to contribute to the knowledge and progress in the scientific domain. The wide range of journals and conferences, each with specific submission requirements, can be difficult to navigate, especially for young scientists without extensive experience. But a suitable publication strategy can be helpful, especially at the beginning of a scientific career. Using the annual conference of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS) e.V. as an example, this editorial highlights fundamental differences, advantages and disadvantages, as well as assistance in selecting the right form of submission.

Clinical trials are carried out to prove the safety and effectiveness of new interventions and therapies. As diseases and their causes continue to become more specific, so do inclusion and exclusion criteria for trials. Patient recruitment has always been a challenge, but with medical progress, it becomes increasingly difficult to achieve the necessary number of cases. In Germany, the Medical Informatics Initiative is planning to use the central application and registration office to conduct feasibility analyses at an early stage and thus to identify suitable project partners. This approach aims to technically adapt/integrate the envisioned infrastructure in such a way that it can be used for trial case number estimation for the planning of multicenter clinical trials. We have developed a fully automated solution called APERITIF that can identify the number of eligible patients based on free-text eligibility criteria, taking into account the MII core data set and based on the FHIR standard. The evaluation showed a precision of 62.64 % for inclusion criteria and a precision of 66.45 % for exclusion criteria.

The current movement in Medical Informatics towards comprehensive Electronic Health Records (EHRs) has enabled a wide range of secondary use cases for this data. However, due to a number of well-justified concerns and barriers, especially with regards to information privacy, access to real medical records by researchers is often not possible, and indeed not always required. An appealing alternative to the use of real patient data is the employment of a generator for realistic, yet synthetic, EHRs. However, we have identified a number of shortcomings in prior works, especially with regards to the adaptability of the projects to the requirements of the German healthcare system. Based on three case studies, we define a non-exhaustive list of requirements for an ideal generator project that can be used in a wide range of localities and settings, to address and enable future work in this regard.

Precision medicine is an emerging and important field for health care. Molecular tumor boards use a combination of clinical and molecular data, such as somatic tumor mutations to decide on personalized therapies for patients who have run out of standard treatment options. Personalized treatment decisions require clinical data from the hospital information system and mutation data to be accessible in a structured way. Here we introduce an open data platform to meet these requirements. We use the openEHR standard to create an expert-curated data model that is stored in a vendor-neutral format. Clinical and molecular patient data is integrated into cBioPortal, a warehousing solution for cancer genomic studies that is extended for use in clinical routine for molecular tumor boards. For data integration, we developed openEHR Mapper, a tool that allows to (i) process input data, (ii) communicate with the openEHR repository, and (iii) export the data to cBioPortal. We benchmarked the mapper performance using XML and JSON as serialization format and added caching capabilities as well as multi-threading to the openEHR Mapper.

Metadata is often used for different tasks in the field of medical informatics: semantic description of data, quality validation, data integration, or information retrieval. Metadata definitions are captured and curated in time-consuming tasks and stored in metadata repositories that manage and preserve the metadata. Due to technical and legal restrictions, metadata is rarely as easily accessible and interoperable as it is necessary for modern information systems. In a previous study, a uniform interface based on the widely used ISO/IEC 11179 and the Facebook data retrieval language GraphQL was introduced as a solution to these technical obstacles. In the meantime, the ISO standard 21526 has been published, a recent version designed with a strong focus on health informatics. While it is conceptually oriented on the metamodel in ISO 11179, a number of extensions but also restructurings have been introduced. In this study, the authors investigated the difference between ISO 11179 and ISO 21526 and extended the unified metadata query interface to be future-proof and in particular, to support the semantic extensions of ISO 21526.

The integration of heterogeneous healthcare data sources is a necessary process to enable the secondary use valuable information in clinical research. Data integration is time-consuming for data stewards. The transformation using predefined rules for data harmonization can reduce the time-consuming and error-prone work and ease the data integration at various sites. In our study, we examined various script(ing) languages to find the most suitable candidate for definition of transformation rules and implement a smart editor which supports the data stewards in selecting rules reusing them. Thereby, it also provides an automatic and seamless documentation to strengthen the reliability of the defined transformation rules.

Background: Secondary use of routine medical data relies on a shared understanding of given information. This understanding is achieved through metadata and their interconnections, which can be stored in metadata repositories (MDRs). The necessity of an MDR is well understood, but the local work on metadata is a time-consuming and challenging process for domain experts., Objective: To support the identification, collection, and provision of metadata in a predefined structured manner to foster consolidation. A particular focus is placed on user acceptance., Methods: We propose a software pipeline MDRBridge as a practical intermediary for metadata capture and processing, based on MDRSheet, an ISO 11179-3 compliant template using popular spreadsheet software. It serves as a practical mediator for metadata acquisition and processing in a broader pipeline. Due to the different origins of the metadata, both manual entry and automatic extractions from application systems are supported. To enable the export of collected metadata into external MDRs, a mapping of ISO 11179 to Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) was developed., Results: MDRSheet is embedded in the processing pipeline MDRBridge and delivers metadata in the CDISC ODM format for further use in MDRs. This approach is used to interactively unify core datasets, import existing standard datasets, and automatically extract all defined data elements from source systems. The involvement of clinical domain experts improved significantly due to minimal changes within their usual work routine., Conclusion: A high degree of acceptance was achieved by adapting the working methods of clinical domain experts. The designed process is capable of transforming all relevant data elements according to the ISO 11179-3 format. MDRSheet is used as an intermediate format to present the information at a glance and to allow editing or supplementing by domain experts., Competing Interests: None declared., (Georg Thieme Verlag KG Stuttgart · New York.)

The utilisation of metadata repositories increasingly promotes secondary use of routinely collected data. However, this has not yet solved the problem of data exchange across organisational boundaries. The local description of a metadata set must also be exchangeable for flawless data exchange. In previous work, a metadata exchange language QL4MDR was developed. This work aimed to examine the applicability of this exchange language. For this purpose, existing MDR implementations were identified and systematically inspected and roughly divided into two categories to distinguish between data integration and query integration. It has been shown that all the implementations can be adapted to QL4MDR. The integration of metadata is an important first step; it enables the exchange of information, which is so urgently needed for the further processing of instance data, from the metadata mappings to the transformation rules.

Scientific challenges based on benchmark data enable the comparison and evaluation of different algorithms and take place regularly in scientific disciplines like medical image processing, text mining or genetics. The idea of a challenge is rarely applied within the eHealth community. Mappathon is a metadata mapping challenge that asks for methods to find corresponding data elements within similar datasets and to correlate data elements among each other.

Background: Heterogeneous healthcare instance data can hardly be integrated without harmonizing its schema-level metadata. Many medical research projects and organizations use metadata repositories to edit, store and reuse data elements. However, existing metadata repositories differ regarding software implementation and have shortcomings when it comes to exchanging metadata. This work aims to define a uniform interface with a technical interlingua between the different MDR implementations in order to enable and facilitate the exchange of metadata, to query over distributed systems and to promote cooperation. To design a unified interface for multiple existing MDRs, a standardized data model must be agreed on. The ISO 11179 is an international standard for the representation of metadata, and since most MDR systems claim to be at least partially compliant, it is suitable for defining an interface thereupon. Therefore, each repository must be able to define which parts can be served and the interface must be able to handle highly linked data. GraphQL is a data access layer and defines query techniques designed to navigate easily through complex data structures., Results: We propose QL 4 MDR, an ISO 11179-3 compatible GraphQL query language. The GraphQL schema for QL 4 MDR is derived from the ISO 11179 standard and defines objects, fields, queries and mutation types. Entry points within the schema define the path through the graph to enable search functionalities, but also the exchange is promoted by mutation types, which allow creating, updating and deleting of metadata. QL 4 MDR is the foundation for the uniform interface, which is implemented in a modern web-based interface prototype., Conclusions: We have introduced a uniform query interface for metadata repositories combining the ISO 11179 standard for metadata repositories and the GraphQL query language. A reference implementation based on the existing Samply.MDR was implemented. The interface facilitates access to metadata, enables better interaction with metadata as well as a basis for connecting existing repositories. We invite other ISO 11179-based metadata repositories to take this approach into account.

The establishment of a digital healthcare system is a national and community task. The Federal Ministry of Education and Research in Germany is providing funding for consortia consisting of university hospitals among others participating in the "Medical Informatics Initiative". Exchange of medical data between research institutions necessitates a place where meta information for this data is made accessible. Within these consortia different metadata registry solutions were chosen. To promote interoperability between these solutions, we have examined whether the portal of Medical Data Models is eligible for managing and communicating metadata and relevant information across different data integration centres of the Medical Informatics Initiative and beyond. Apart from the MDM-portal, some ISO 11179-based systems such as Samply.MDR as well as openEHR-based solutions are going to be applyed. In this paper, we have focused on the creation of a mapping model between the CDISC ODM standard and the Samply.MDR import format. In summary, it can be stated that the mapping model is feasible and promote the exchangeability between different metadata registry approaches.

Secondary use of healthcare data is dependent on the availability of provenance data for assessing its quality, reliability or trustworthiness. Usually, instance-level data that might be communicated by HL7 interfaces entail limited metadata about involved software systems, persons or organizations bearing responsibility for those systems. This paper proposes a strategy for capturing interoperable provenance data needed by data stewards for assessing healthcare data that are reused in a research context. Aimed at a realistic level of granularity even system-level metadata will support a data steward trying to trace the origins or provenance of healthcare data that have been transferred to the research context. Those metadata are extracted from the 3LGM2-system, used for modelling hospital information systems. Based on the W3C provenance specification interrelated activities, entities and agents can be integrated and stored in RDF triple stores and therefore queried and visualized.

To exchange data across several sites or to interpret it at a later point in time, it is necessary to create a general understanding of the data. As a standard practice, this understanding is achieved through metadata. These metadata are usually stored in relational databases, so-called metadata repositories (MDR). Typical functions of such an MDR include pure storage, administration and other specific metadata functionalities such as finding relations among data elements. This results in a multitude of connections between the data elements, which can be described as highly interconnected graphs. To use alternative databases such as graph databases for modelling and visualisation it has already been proven to be beneficial in previous studies. The objective of this work is to evaluate how on-board techniques rely on matching and mapping using a graph database. Different datasets relating to cancer were entered, and algorithms for metadata management were applied.

The public Medical Data Models (MDM) portal with more than 9.000 annotated forms from clinical trials and other sources provides many research opportunities for the medical informatics community. It is mainly used to address the problem of heterogeneity by searching, mediating, reusing, and assessing data models, e. g. the semi-interactive curation of core data records in a special domain. Furthermore, it can be used as a benchmark for evaluating algorithms that create, transform, annotate, and analyse structured patient data. Using CDISC ODM for syntactically representing all data models in the MDM portal, there are semi-automatically added UMLS CUIs at several ODM levels like ItemGroupDef, ItemDef, or CodeList item. This can improve the interpretability and processability of the received information, but only if the coded information is correct and reliable. This raises the question how to assure that semantically similar datasets are also processed and classified similarly. In this work, a (semi-)automatic approach to analyse and assess items, questions, and data elements in clinical studies is described. The approach uses a hybrid evaluation process to rate and propose semantic annotations for under-specified trial items. The evaluation algorithm operates with the commonly used NLM MetaMap to provide UMLS support and corpus-based proposal algorithms to link datasets from the provided CDISC ODM item pool.

Clinical care and research data are widely dispersed in isolated systems based on heterogeneous data models. Biomedicine predominantly makes use of connected datasets based on the Semantic Web paradigm. Initiatives like Bio2RDF created Resource Description Framework (RDF) versions of Omics resources, enabling sophisticated Linked Data applications. In contrast, electronic healthcare records (EHR) data are generated and processed in diverse clinical subsystems within hospital information systems (HIS). Usually, each of them utilizes a relational database system with a different proprietary schema. Semantic integration and access to the data is hardly possible. This paper describes ways of using Ontology Based Data Access (OBDA) for bridging the semantic gap between existing raw data and user-oriented views supported by ontology-based queries. Based on mappings between entities of data schemas and ontologies data can be made available as materialized or virtualized RDF triples ready for querying and processing. Our experiments based on CentraXX for biobank and study management demonstrate the advantages of abstracting away from low level details and semantic mediation. Furthermore, it becomes clear that using a professional platform for Linked Data applications is recommended due to the inherent complexity, the inconvenience to confront end users with SPARQL, and scalability and performance issues.