Understanding the Nature of Metadata: Systematic Review.

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.

Definition, Composition, and Harmonization of Core Datasets Within the German Center for Lung Research.

Core datasets are the composition of essential data items for a certain research scope. As they state commonalities between heterogeneous data collections, they serve as a basis for cross-site and cross-disease research. Therefore, researchers at the national and international levels have addressed the problem of missing core datasets. The German Center for Lung Research (DZL) comprises five sites and eight disease areas and aims to gain further scientific knowledge by continuously promoting collaborations. In this study, we elaborated a methodology for defining core datasets in the field of lung health science. Additionally, through support of domain experts, we have utilized our method and compiled core datasets for each DZL disease area and a general core dataset for lung research. All included data items were annotated with metadata and where possible they were assigned references to international classification systems. Our findings will support future scientific collaborations and meaningful data collections.

Proactive authenticated notifications for health practitioners: two way human computer interaction through phone.

Notifications and alerts play an important role in clinical daily routine. Rising prevalence of clinical decision support systems and electronic health records also result in increasing demands on notification systems. Failure adequately to communicate a critical value is a potential cause of adverse events. Critical laboratory values and changing vital data depend on timely notifications of medical staff. Vital monitors and medical devices rely on acoustic signals for alerting which are prone to "alert fatigue" and require medical staff to be present within audible range. Personal computers are unsuitable to display time critical notification messages, since the targeted medical staff are not always operating or watching the computer. On the other hand, mobile phones and smart devices enjoy increasing popularity. Previous notification systems sending text messages to mobile phones depend on asynchronous confirmations. By utilizing an automated telephony server, we provide a method to deliver notifications quickly and independently of the recipients' whereabouts while allowing immediate feedback and confirmations. Evaluation results suggest the feasibility of the proposed notification system for real-time notifications.

The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation.

BACKGROUND: In the field of medicine and medical informatics, the importance of comprehensive metadata has long been recognized, and the composition of metadata has become its own field of profession and research. To ensure sustainable and meaningful metadata are maintained, standards and guidelines such as the FAIR (Findability, Accessibility, Interoperability, Reusability) principles have been published. The compilation and maintenance of metadata is performed by field experts supported by metadata management apps. The usability of these apps, for example, in terms of ease of use, efficiency, and error tolerance, crucially determines their benefit to those interested in the data. OBJECTIVE: This study aims to provide a metadata management app with high usability that assists scientists in compiling and using rich metadata. We aim to evaluate our recently developed interactive web app for our collaborative metadata repository (CoMetaR). This study reflects how real users perceive the app by assessing usability scores and explicit usability issues. METHODS: We evaluated the CoMetaR web app by measuring the usability of 3 modules: core module, provenance module, and data integration module. We defined 10 tasks in which users must acquire information specific to their user role. The participants were asked to complete the tasks in a live web meeting. We used the System Usability Scale questionnaire to measure the usability of the app. For qualitative analysis, we applied a modified think aloud method with the following thematic analysis and categorization into the ISO 9241-110 usability categories. RESULTS: A total of 12 individuals participated in the study. We found that over 97% (85/88) of all the tasks were completed successfully. We measured usability scores of 81, 81, and 72 for the 3 evaluated modules. The qualitative analysis resulted in 24 issues with the app. CONCLUSIONS: A usability score of 81 implies very good usability for the 2 modules, whereas a usability score of 72 still indicates acceptable usability for the third module. We identified 24 issues that serve as starting points for further development. Our method proved to be effective and efficient in terms of effort and outcome. It can be adapted to evaluate apps within the medical informatics field and potentially beyond.

HIStream-Import: A Generic ETL Framework for Processing Arbitrary Patient Data Collections or Hospital Information Systems into HL7 FHIR Bundles.

Data integration is a necessary and important step to perform translational research and improve the sample size beyond single data collections. For health information, the most recent established communication standards is HL7 FHIR. To bridge the concepts of "minimal invasive" data integration and open standards, we propose a generic ETL framework to process arbitrary patient related data collections into HL7 FHIR - which in turn can then be used for loading into target data warehouses. The proposed algorithm is able to read any relational delimited text exports and produce a standard HL7 FHIR bundle collection. We evaluated an implementation of the algorithm using different lung research registries and used the resulting FHIR resources to fill our i2b2 based data warehouse as well an OMOP common data model repository.

ISO 21526 Conform Metadata Editor for FAIR Unicode SKOS Thesauri.

Metadata repositories are an indispensable component of data integration infrastructures and support semantic interoperability between knowledge organization systems. Standards for metadata representation like the ISO/IEC 11179 as well as the Resource Description Framework (RDF) and the Simple Knowledge Organization System (SKOS) by the World Wide Web Consortium were published to ensure metadata interoperability, maintainability and sustainability. The FAIR guidelines were composed to explicate those aspects in four principles divided in fifteen sub-principles. The ISO/IEC 21526 standard extends the 11179 standard for the domain of health care and mandates that SKOS be used for certain scenarios. In medical informatics, the composition of health care SKOS classification schemes is often managed by documentalists and data scientists. They use editors, which support them in producing comprehensive and valid metadata. Current metadata editors either do not properly support the SKOS resource annotations, require server applications or make use of additional databases for metadata storage. These characteristics are contrary to the application independency and versatility of raw Unicode SKOS files, e.g. the custom text arrangement, extensibility or copy & paste editing. We provide an application that adds navigation, auto completion and validity check capabilities on top of a regular Unicode text editor.

Data Integration into OMOP CDM for Heterogeneous Clinical Data Collections via HL7 FHIR Bundles and XSLT.

Data integration is an important task in medical informatics and highly impacts the gain out of existing health information data. These tasks are using implemented as extract transform and load processes. By introducing HL7 FHIR as an intermediate format, our aim was to integrate heterogeneous data from a German pulmonary hypertension registry into an OMOP Common Data Model. First, domain knowledge experts defined a common parameter set, which was subsequently mapped to standardized terminologies like LOINC or SNOMED-CT. Data was extracted as HL7 FHIR Bundle to be transformed to OMOP CDM by using XSLT. We successfully transformed the majority of data elements to the OMOP CDM in a feasible time.

Verifying Data Integration Configurations for Semantical Correctness and Completeness.

Data integration is the problem of combining data residing at different sources and providing the user with a unified view of these data. In medical informatics, such a unified view enables retrospective analyses based on more facts and prospective recruitment of more patients than any single data collection by itself. The technical part of data integration is based on rules interpreted by software. These rules define how to perform the translation of source database schemata into the target database schema. Translation rules are formulated by data managers who usually do not have the knowledge about meaning and acquisition methods of the data they handle. The professionals (data providers) collecting the source data who have the respective knowledge again usually have no sufficient technical background. Since data providers are neither able to formulate the transformation rules themselves nor able to validate them, the whole process is fault-prone. Additionally, in continuous development and maintenance of (meta-) data repositories, data structures underlie changes, which may lead to outdated transformation rules. We did not find any technical solution, which enables data providers to formulate transformation rules themselves or which provides an understandable reflection of given rules. Our approach is to enable data providers understand the rules regarding their own data by presenting rules and available context visually. Context information is fetched from a metadata repository. In this paper, we propose a software tool that builds on existing data integration infrastructures. The tool provides a visually supported validation routine for data integration rules. In a first step towards its evaluation, we implement the tool into the DZL data integration process and verify the correct presentation of transformation rules.

Provenance for Biomedical Ontologies with RDF and Git.

The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. In order to enable consortium-wide retrospective research and prospective patient recruitment, we perform data integration into a central data warehouse. The enhancements of the underlying ontology is an ongoing process for which we developed the Collaborative Metadata Repository (CoMetaR) tool. Its technical infrastructure is based on the Resource Description Framework (RDF) for ontology representation and the distributed version control system Git for storage and versioning. Ontology development involves a considerable amount of data curation. Data provenance improves its feasibility and quality. Especially in collaborative metadata development, a comprehensive annotation about "who contributed what, when and why" is essential. Although RDF and Git versioning repositories are commonly used, no existing solution captures metadata provenance information in sufficient detail. We propose an enhanced composition of standardized RDF statements for detailed provenance representation. Additionally, we developed an algorithm that extracts and translates provenance data from the repository into the proposed RDF statements.

Metadata Import from RDF to i2b2.

Metadata management is an important task in medical informatics and highly affects the gain out of existing health information data. Data Warehouse solutions like Informatics for Integrating Biology and the Bedside (i2b2) are common tools for identifying patient cohorts and analyzing collected clinical data while respecting patient privacy. The Resource Description Framework (RDF) is designed for highly interoperable ontology representation in various formats, facilitating ontology and metadata management. Our approach is to combine i2b2's and RDF's benefits by importing the easy-to-edit RDF ontology into the extensive-research-enabling i2b2 software. We do so by using a SPARQL Protocol and RDF Query Language (SPARQL) interface, that enables RDF data queries, and developing a java program, which then generates i2b2-specific SQL insert statements. To demonstrate our solution's feasibility, we transcribe our lung disease specific ontology to RDF and import it into our i2b2 data warehouse.

Using RDF and Git to Realize a Collaborative Metadata Repository.

The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. The participating study sites' register data differs in terms of software and coding system as well as data field coverage. To perform meaningful consortium-wide queries through one single interface, a uniform conceptual structure is required covering the DZL common data elements. No single existing terminology includes all our concepts. Potential candidates such as LOINC and SNOMED only cover specific subject areas or are not granular enough for our needs. To achieve a broadly accepted and complete ontology, we developed a platform for collaborative metadata management. The DZL data management group formulated detailed requirements regarding the metadata repository and the user interfaces for metadata editing. Our solution builds upon existing standard technologies allowing us to meet those requirements. Its key parts are RDF and the distributed version control system Git. We developed a software system to publish updated metadata automatically and immediately after performing validation tests for completeness and consistency.

Data Discovery for Integration of Heterogeneous Medical Datasets in the German Center for Lung Research (DZL).

The German Centre for Lung Research (DZL) is an association of Germany's leading university and non-university institutions dedicated to lung research. Institutes and disease areas within the DZL manage their own data in several databases and registers using different software tools. Aim of our data integration effort is to provide a single central data warehouse frontend, where all patient related data is combined and made accessible. A two-stage survey was used to determine the data collections suitable for data integration. Integration was performed via extract-transform-load (ETL) steps using custom software. Original software (e.g. eCRF) used by the data collections did not need any modifications. The survey yielded 68 data collections. Until Jan 2018, 20 collections were successfully integrated. 10 collections were withdrawn by their owners while the integration of 38 was delayed. Data discovery, the process of finding existing data collections in a large research network, proved to be the step most underestimated. From technical point of view, data integration proved to be of minor complexity in comparison to the effort required for harmonization/mapping of data elements and management of common terminology.

Li2b2-Façade: Simulation of i2b2 Data Warehouse Server and Client for Interaction with Other Systems.

Since its release in 2004, the i2b2 data warehouse software has become a valuable tool for clinical researchers. Physicians can use its browser-based query frontend intuitively without additional training or reading through documentation. While the i2b2 software describes it's API as "REST", it is neither stateless nor does it follow the common guidelines for RESTful APIs. Thus, interfacing other software with i2b2's custom RPC-style XML-API is a very cumbersome process. To overcome these issues, we developed a lightweight software abstraction layer "lightweight i2b2 façade" (li2b2-façade).

CoMetaR: A Collaborative Metadata Repository for Biomedical Research Networks.

The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. To perform consortium-wide queries through one single interface, it requires a uniform conceptual structure. No single terminology covers all our concepts. To achieve a broadly accepted and complete ontology, we developed a platform for collaborative metadata management "CoMetaR". Anyone can browse and discuss the ontology while editing can be performed by authenticated users.

Asynchronous Query Distribution Between Multiple i2b2 Research Data Warehouses: Li2b2-SHRINE.

Clinical Data Ware Houses are established sources for research and quality management. The open source data warehouse software i2b2 enjoys good reputation and wide-spread use in the international medical informatics community. We developed a novel infrastructure to allow queries to be distributed asynchronously between i2b2 data warehouses.

Proof-of-Concept Integration of Heterogeneous Biobank IT Infrastructures into a Hybrid Biobanking Network.

Cross-institutional biobank networks hold the promise of supporting medicine by enabling the exchange of associated samples for research purposes. Various initiatives, such as BBMRI-ERIC and German Biobank Node (GBN), aim to interconnect biobanks for enabling the compilation of joint biomaterial collections. However, building software platforms to facilitate such collaboration is challenging due to the heterogeneity of existing biobank IT infrastructures and the necessary efforts for installing and maintaining additional software components. As a remedy, this paper presents the concept of a hybrid network for interconnecting already existing software components commonly found in biobanks and a proof-of-concept implementation of two prototypes involving four biobanks of the German Biobank Node. Here we demonstrate the successful bridging of two IT systems found in many German biobanks - Samply and i2b2.

ChronoQuery: visual modelling of temporal queries for real-time decision support.

Clinical decision support systems are an important aspect of medical informatics. The increasing amount of available patient data requires physicians to rely on information technology for research and during their day by day work. In intensive care medicine, fast actions are especially important. One major step towards enabling direct interaction of medical staff with patient data was the development of clinical data repositories with easy query frontends. While clinical data repositories can be extended for the use of real-time data, the corresponding query frontends do not support the time concepts necessary for real-time queries and decision support. Aim of this project is the development of a user interface to give physicians visual understanding of propositional logic combined with time concepts. Thus, physicians should be able formulate simple time based queries on their own--and validate and quality check complex queries created by medical informatics experts.

Architecture of a prehospital emergency patient care report system (PEPRS).

In recent years, prehospital emergency care adapted to the technology shift towards tablet computers and mobile computing. In particular, electronic patient care report (e-PCR) systems gained considerable attention and adoption in prehospital emergency medicine [1]. On the other hand, hospital information systems are already widely adopted. Yet, there is no universal solution for integrating prehospital emergency reports into electronic medical records of hospital information systems. Previous projects either relied on proprietary viewing workstations or examined and transferred only data for specific diseases (e.g. stroke patients[2]). Using requirements engineering and a three step software engineering approach, this project presents a generic architecture for integrating prehospital emergency care reports into hospital information systems. Aim of this project is to describe a generic architecture which can be used to implement data transfer and integration of pre hospital emergency care reports to hospital information systems. In summary, the prototype was able to integrate data in a standardized manner. The devised methods can be used design generic software for prehospital to hospital data integration.