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.

Objective: Convert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR.Materials and Methods: FHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide.Results: The modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915 000 resources. The full dataset contained 315 000 patients covering approximately 5 840 000 resources. The final datasets in NDJSON format are accessible on PhysioNet.Discussion: Our work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work.Conclusion: The newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.

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.

The new medical device communication protocol known as IEEE 11073 SDC is well-suited for the integration of (surgical) point-of-care devices, so are the established Health Level Seven (HL7) V2 and Digital Imaging and Communications in Medicine (DICOM) standards for the communication of systems in the clinical IT infrastructure (CITI). An integrated operating room (OR) and other integrated clinical environments, however, need interoperability between both domains to fully unfold their potential for improving the quality of care as well as clinical workflows. This work thus presents concepts for the propagation of clinical and administrative data to medical devices, physiologic measurements and device parameters to clinical IT systems, as well as image and multimedia content in both directions. Prototypical implementations of the derived components have proven to integrate well with systems of networked medical devices and with the CITI, effectively connecting these heterogeneous domains. Our qualitative evaluation indicates that the interoperability concepts are suitable to be integrated into clinical workflows and are expected to benefit patients and clinicians alike. The upcoming HL7 Fast Healthcare Interoperability Resources (FHIR) communication standard will likely change the domain of clinical IT significantly. A straightforward mapping to its resource model thus ensures the tenability of these concepts despite a foreseeable change in demand and requirements.

Background: In patient care, data are historically generated and stored in heterogeneous databases that are domain specific and often noninteroperable or isolated. As the amount of health data increases, the number of isolated data silos is also expected to grow, limiting the accessibility of the collected data. Medical informatics is developing ways to move from siloed data to a more harmonized arrangement in information architectures. This paradigm shift will allow future research to integrate medical data at various levels and from various sources. Currently, comprehensive requirements engineering is working on data integration projects in both patient care– and research-oriented contexts, and it is significantly contributing to the success of such projects. In addition to various stakeholder-based methods, document-based requirement elicitation is a valid method for improving the scope and quality of requirements. Objective: Our main objective was to provide a general catalog of functional requirements for integrating medical data into knowledge management environments. We aimed to identify where integration projects intersect to derive consistent and representative functional requirements from the literature. On the basis of these findings, we identified which functional requirements for data integration exist in the literature and thus provide a general catalog of requirements. Methods: This work began by conducting a literature-based requirement elicitation based on a broad requirement engineering approach. Thus, in the first step, we performed a web-based systematic literature review to identify published articles that dealt with the requirements for medical data integration. We identified and analyzed the available literature by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. In the second step, we screened the results for functional requirements using the requirements engineering method of document analysis and derived the requirements into a uniform requirement syntax. Finally, we classified the elicited requirements into a category scheme that represents the data life cycle. Results: Our 2-step requirements elicitation approach yielded 821 articles, of which 61 (7.4%) were included in the requirement elicitation process. There, we identified 220 requirements, which were covered by 314 references. We assigned the requirements to different data life cycle categories as follows: 25% (55/220) to data acquisition, 35.9% (79/220) to data processing, 12.7% (28/220) to data storage, 9.1% (20/220) to data analysis, 6.4% (14/220) to metadata management, 2.3% (5/220) to data lineage, 3.2% (7/220) to data traceability, and 5.5% (12/220) to data security. Conclusions: The aim of this study was to present a cross-section of functional data integration–related requirements defined in the literature by other researchers. The aim was achieved with 220 distinct requirements from 61 publications. We concluded that scientific publications are, in principle, a reliable source of information for functional requirements with respect to medical data integration. Finally, we provide a broad catalog to support other scientists in the requirement elicitation phase.

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. We propose QL4MDR, an ISO 11179-3 compatible GraphQL query language. The GraphQL schema for QL4MDR 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. QL4MDR is the foundation for the uniform interface, which is implemented in a modern web-based interface prototype. 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.