Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review.

BACKGROUND: Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes. OBJECTIVE: In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata. METHODS: We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization. RESULTS: Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases. CONCLUSIONS: Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data.

3LGM2IHE: Requirements for Data-Protection-Compliant Research Infrastructures-A Systematic Comparison of Theory and Practice-Oriented Implementation.

OBJECTIVES: The TMF (Technology, Methods, and Infrastructure for Networked Medical Research) Data Protection Guide (TMF-DP) makes path-breaking recommendations on the subject of data protection in research projects. It includes comprehensive requirements for applications such as patient lists, pseudonymization services, and consent management services. Nevertheless, it lacks a structured, categorized list of requirements for simplified application in research projects and systematic evaluation. The 3LGM2IHE ("Three-layer Graphbased meta model - Integrating the Healthcare Enterprise [IHE] " ) project is funded by the German Research Foundation (DFG). 3LGM2IHE aims to define modeling paradigms and implement modeling tools for planning health care information systems. In addition, one of the goals is to create and publish 3LGM2 information system architecture design patterns (short "design patterns") for the community as design models in terms of a framework. A structured list of data protection-related requirements based on the TMF-DP is a precondition to integrate functions (3LGM2 Domain Layer) and building blocks (3LGM2 Logical Tool Layer) in 3LGM2 design patterns. METHODS: In order to structure the continuous text of the TMF-DP, requirement types were defined in a first step. In a second step, dependencies and delineations of the definitions were identified. In a third step, the requirements from the TMF-DP were systematically extracted. Based on the identified lists of requirements, a fourth step included the comparison of the identified requirements with exemplary open source tools as provided by the "Independent Trusted Third Party of the University Medicine Greifswald" (TTP tools). RESULTS: As a result, four lists of requirements were created, which contain requirements for the "patient list", the "pseudonymization service", and the "consent management", as well as cross-component requirements from the TMF-DP chapter 6 in a structured form. Further to requirements (1), possible variants (2) of implementations (to fulfill a single requirement) and recommendations (3) were identified. A comparison of the requirements lists with the functional scopes of the open source tools E-PIX (record linkage), gPAS (pseudonym management), and gICS (consent management) has shown that these fulfill more than 80% of the requirements. CONCLUSIONS: A structured set of data protection-related requirements facilitates a systematic evaluation of implementations with respect to the fulfillment of the TMF-DP guidelines. These re-usable lists provide a decision aid for the selection of suitable tools for new research projects. As a result, these lists form the basis for the development of data protection-related 3LGM2 design patterns as part of the 3LGM2IHE project.

Opportunities of Digital Infrastructures for Disease Management-Exemplified on COVID-19-Related Change in Diagnosis Counts for Diabetes-Related Eye Diseases.

Background: Retrospective research on real-world data provides the ability to gain evidence on specific topics especially when running across different sites in research networks. Those research networks have become increasingly relevant in recent years; not least due to the special situation caused by the COVID-19 pandemic. An important requirement for those networks is the data harmonization by ensuring the semantic interoperability. Aims: In this paper we demonstrate (1) how to facilitate digital infrastructures to run a retrospective study in a research network spread across university and non-university hospital sites; and (2) to answer a medical question on COVID-19 related change in diagnostic counts for diabetes-related eye diseases. Materials and methods: The study is retrospective and non-interventional and runs on medical case data documented in routine care at the participating sites. The technical infrastructure consists of the OMOP CDM and other OHDSI tools that is provided in a transferable format. An ETL process to transfer and harmonize the data to the OMOP CDM has been utilized. Cohort definitions for each year in observation have been created centrally and applied locally against medical case data of all participating sites and analyzed with descriptive statistics. Results: The analyses showed an expectable drop of the total number of diagnoses and the diagnoses for diabetes in general; whereas the number of diagnoses for diabetes-related eye diseases surprisingly decreased stronger compared to non-eye diseases. Differences in relative changes of diagnoses counts between sites show an urgent need to process multi-centric studies rather than single-site studies to reduce bias in the data. Conclusions: This study has demonstrated the ability to utilize an existing portable and standardized infrastructure and ETL process from a university hospital setting and transfer it to non-university sites. From a medical perspective further activity is needed to evaluate data quality of the utilized real-world data documented in routine care and to investigate its eligibility of this data for research.

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.

Toolbox for Research, or how to facilitate a central data management in small-scale research projects.

BACKGROUND: In most research projects budget, staff and IT infrastructures are limiting resources. Especially for small-scale registries and cohort studies professional IT support and commercial electronic data capture systems are too expensive. Consequently, these projects use simple local approaches (e.g. Excel) for data capture instead of a central data management including web-based data capture and proper research databases. This leads to manual processes to merge, analyze and, if possible, pseudonymize research data of different study sites. RESULTS: To support multi-site data capture, storage and analyses in small-scall research projects, corresponding requirements were analyzed within the MOSAIC project. Based on the identified requirements, the Toolbox for Research was developed as a flexible software solution for various research scenarios. Additionally, the Toolbox facilitates data integration of research data as well as metadata by performing necessary procedures automatically. Also, Toolbox modules allow the integration of device data. Moreover, separation of personally identifiable information and medical data by using only pseudonyms for storing medical data ensures the compliance to data protection regulations. This pseudonymized data can then be exported in SPSS format in order to enable scientists to prepare reports and analyses. CONCLUSIONS: The Toolbox for Research was successfully piloted in the German Burn Registry in 2016 facilitating the documentation of 4350 burn cases at 54 study sites. The Toolbox for Research can be downloaded free of charge from the project website and automatically installed due to the use of Docker technology.