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.

Admission rates and clinical profiles of children and youth with eating disorders treated as inpatients before and during the COVID-19 pandemic in a German university hospital.

Introduction: Children and youth at risk for mental health disorders, such as eating disorders (ED), were particularly affected by the COVID-19 pandemic, yet evidence for the most seriously affected and thus hospitalized youth in Germany is scarce. Methods: This crosssectional study investigated anonymized routine hospital data (demographic information, diagnoses, treatment modalities) of patients admitted (n = 2,849) to the Department of Child and Adolescence Psychiatry, Psychosomatics and Psychotherapy (DCAPPP) of a German University Hospital between 01/2016 and 02/2022. Absolute and relative number of inpatients with or without ED prior to (01/2016-02/2020) and during the COVID-19 pandemic (03/2020-02/2022) were compared. The effect of school closures as part of social lockdown measures for COVID-19 mitigation on inpatient admission rate was explored as it has been discussed as a potential risk factor for mental health problems in youth. Results: During the COVID-19 pandemic, ED inpatient admission rate increased from 10.5 to 16.7%, primarily driven by Anorexia Nervosa (AN). In contrast to previous reports, we found no change in somatic and mental disorder comorbidity, age or sexratio for hospitalized youth with ED. However, we did observe a shortened length of hospital stay (LOS) for hospitalized youth with and without ED. In addition, non-ED admissions presented with an increased number of mental disorder comorbidities. In contrast to our hypothesis, school closures were not related to the observed increase in ED. Discussion: In summary, the COVID-19 pandemic was associated with an increased rate of inpatient treatment for youth suffering from AN, and of youth affected by multiple mental disorders. Accordingly, we assume that inpatient admission was prioritized for individuals with a higher burden of disease during the COVID-19 pandemic. Our findings pinpoint the need for adequate inpatient mental health treatment capacities during environmental crises, and a further strengthening of child and adolescence psychiatry services in Germany.

The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study.

BACKGROUND: In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status. OBJECTIVE: Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data. METHODS: In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. RESULTS: Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies. CONCLUSIONS: The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.

CODEX Meets RACOON - A Concept for Collaborative Documentation of Clinical and Radiological COVID-19 Data.

Within the scope of the two NUM projects CODEX and RACOON we developed a preliminary technical concept for documenting clinical and radiological COVID-19 data in a collaborative approach and its preceding findings of a requirement analysis. At first, we provide an overview of NUM and its two projects CODEX and RACOON including the GECCO data set. Furthermore, we demonstrate the foundation for the increased collaboration of both projects, which was additionally supported by a survey conducted at University Hospital Frankfurt. Based on the survey results mint Lesion™, developed by Mint Medical and used at all project sites within RACOON, was selected as the "Electronic Data Capture" (EDC) system for CODEX. Moreover, to avoid duplicate entry of GECCO data into both EDC systems, an early effort was made to consider a collaborative and efficient technical approach to reduce the workload for the medical documentalists. As a first effort we present a preliminary technical concept representing the current and possible future data workflow of CODEX and RACOON. This concept includes a software component to synchronize GECCO data sets between the two EDC systems using the HL7 FHIR standard. Our first approach of a collaborative use of an EDC system and its medical documentalists could be beneficial in combination with the presented synchronization component for all participating project sites of CODEX and RACOON with regard to an overall reduced documentation workload.

Development of an Interactive Dashboard for OSSE Rare Disease Registries.

BACKGROUND: The Open Source Registry System for Rare Diseases (OSSE), a web-based tool to create rare disease patient registries, currently offers no possibility to view aggregated registry data within the system. Here, we present the development and implementation of a dashboard for the registry of the German NEOCYST (Network for early onset cystic kidney diseases) consortium. METHODS: Based on user requirements from NEOCYST, we developed a general dashboard for all OSSE registries, which was extended with NEOCYST-specific statistics. RESULTS: The dashboard now allows users to gain a quick overview of key data, such as patient counts or the availability of biospecimens. CONCLUSION: This work represents a first prototypical approach for an OSSE dashboard, demonstrated in an existing rare disease registry, to be further evaluated and enhanced in the future.

Domain-Specific Common Data Elements for Rare Disease Registration: Conceptual Approach of a European Joint Initiative Toward Semantic Interoperability in Rare Disease Research.

BACKGROUND: With hundreds of registries across Europe, rare diseases (RDs) suffer from fragmented knowledge, expertise, and research. A joint initiative of the European Commission Joint Research Center and its European Platform on Rare Disease Registration (EU RD Platform), the European Reference Networks (ERNs), and the European Joint Programme on Rare Diseases (EJP RD) was launched in 2020. The purpose was to extend the set of common data elements (CDEs) for RD registration by defining domain-specific CDEs (DCDEs). OBJECTIVE: This study aims to introduce and assess the feasibility of the concept of a joint initiative that unites the efforts of the European Platform on Rare Disease Registration Platform, ERNs, and European Joint Programme on Rare Diseases toward extending RD CDEs, aiming to improve the semantic interoperability of RD registries and enhance the quality of RD research. METHODS: A joint conference was conducted in December 2020. All 24 ERNs were invited. Before the conference, a survey was communicated to all ERNs, proposing 18 medical domains and requesting them to identify highly relevant choices. After the conference, a 3-phase plan for defining and modeling DCDEs was drafted. Expected outcomes included harmonized lists of DCDEs. RESULTS: All ERNs attended the conference. The survey results indicated that genetic, congenital, pediatric, and cancer were the most overlapping domains. Accordingly, the proposed list was reorganized into 10 domain groups and recommunicated to all ERNs, aiming at a smaller number of domains. CONCLUSIONS: The approach described for defining DCDEs appears to be feasible. However, it remains dynamic and should be repeated regularly based on arising research needs.

Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository.

BACKGROUND: Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium's partner sites. OBJECTIVES: Major limitations of the former approach included manual interpretation of the results and hard coding of analyses, making their expansion to new data elements and databases time-consuming and error prone. We here present an enhanced version of the DQA tool by linking it to common data element definitions stored in a metadata repository (MDR), adopting the harmonized DQA framework from Kahn et al and its application within the MIRACUM consortium. METHODS: Data quality checks were consequently aligned to a harmonized DQA terminology. Database-specific information were systematically identified and represented in an MDR. Furthermore, a structured representation of logical relations between data elements was developed to model plausibility-statements in the MDR. RESULTS: The MIRACUM DQA tool was linked to data element definitions stored in a consortium-wide MDR. Additional databases used within MIRACUM were linked to the DQ checks by extending the respective data elements in the MDR with the required information. The evaluation of DQ checks was automated. An adaptable software implementation is provided with the R package DQAstats. CONCLUSION: The enhancements of the DQA tool facilitate the future integration of new data elements and make the tool scalable to other databases and data models. It has been provided to all ten MIRACUM partners and was successfully deployed and integrated into their respective data integration center infrastructure.

The Registry Data Warehouse in the European Reference Network for Rare Respiratory Diseases - Background, Conception and Implementation.

Rare lung diseases affect 1.5-3 million people in Europe while causing bad prognosis or early deaths for patients. The European Reference Network for Respiratory Diseases (ERN-Lung) is a patient centric network, funded by the European Union (EU). The aims of ERN-LUNG is to increase healthcare and research regarding rare respiratory diseases. An initial need for cross-border healthcare and research is the use of registries and databases. A typical problem in registries for RDs is the data exchange, since the registries use different kind of data with different types or descriptions. Therefore, ERN-Lung decided to create a new Registry Data-Warehouse (RDW) where different existing registries are connected to enable cross-border healthcare within ERN-Lung. This work facilitates the aims, conception and implementation for the RDW, while considering a semantic interoperability approach. We created a common dataset (CDS) to have a common descriptions of respiratory diseases patients within the ERN registries. We further developed the RDW based on Open Source Registry System for Rare Diseases (OSSE), which includes a Metadata Repository with the Samply.MDR to unique describe data for the minimal dataset. Within the RDW, data from existing registries is not stored in a central database. The RDW uses the approach of the "Decentral Search" and can send requests to the connected registries, whereas only aggregated data is returned about how many patients with specific characteristics are available. However, further work is needed to connect the different existing registries to the RDW and to perform first studies.

Visualization of Similar Patients in a Clinical Decision Support System for Rare Diseases - A Focus Group Study.

The diagnosis of patients with rare diseases is often delayed. A Clinical Decision Support System using similarity analysis of patient-based data may have the potential to support the diagnosis of patients with rare diseases. This qualitative study has the objective to investigate how the result of a patient similarity analysis should be presented to a physician to enable diagnosis support. We conducted a focus group with physicians practicing in rare diseases as well as medical informatics researchers. To prepare the focus group, a literature search was performed to check the current state of research regarding visualization of similar patients. We then created software-mockups for the presentation of these visualization methods for the discussion within the focus group. Two persons took independently field notes for data collection of the focus group. A questionnaire was distributed to the participants to rate the visualization methods. The results show that four visualization methods are promising for the visualization of similar patients: "Patient on demand table", "Criteria selection", "Time-Series chart" and "Patient timeline. "Patient on demand table" shows a direct comparison of patient characteristics, whereas "Criteria selection" allows the selection of different patient criteria to get deeper insights into the data. The "Time-Series chart" shows the time course of clinical parameters (e.g. blood pressure) whereas a "Patient timeline" indicates which time events exist for a patient (e.g. several symptoms on different dates). In the future, we will develop a software-prototype of the Clinical Decision Support System to include the visualization methods and evaluate the clinical usage.

How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system.

BACKGROUND: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain. RESULTS: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software Open-Source Registry System for Rare Diseases (OSSE) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded. CONCLUSIONS: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some undiagnosed patients.

Enabling External Inquiries to an Existing Patient Registry by Using the Open Source Registry System for Rare Diseases: Demonstration of the System Using the European Society for Immunodeficiencies Registry.

BACKGROUND: The German Network on Primary Immunodeficiency Diseases (PID-NET) utilizes the European Society for Immunodeficiencies (ESID) registry as a platform for collecting data. In the context of PID-NET data, we show how registries based on custom software can be made interoperable for better collaborative access to precollected data. The Open Source Registry System for Rare Diseases (Open-Source-Registersystem für Seltene Erkrankungen [OSSE], in German) provides patient organizations, physicians, scientists, and other parties with open source software for the creation of patient registries. In addition, the necessary interoperability between different registries based on the OSSE, as well as existing registries, is supported, which allows those registries to be confederated at both the national and international levels. OBJECTIVE: Data from the PID-NET registry should be made available in an interoperable manner without losing data sovereignty by extending the existing custom software of the registry using the OSSE registry framework. METHODS: This paper describes the following: (1) the installation and configuration of the OSSE bridgehead, (2) an approach using a free toolchain to set up the required interfaces to connect a registry with the OSSE bridgehead, and (3) the decentralized search, which allows the formulation of inquiries that are sent to a selected set of registries of interest. RESULTS: PID-NET uses the established and highly customized ESID registry software. By setting up a so-called OSSE bridgehead, PID-NET data are made interoperable according to a federated approach, and centrally formulated inquiries for data can be received. As the first registry to use the OSSE bridgehead, the authors introduce an approach using a free toolchain to efficiently implement and maintain the required interfaces. Finally, to test and demonstrate the system, two inquiries are realized using the graphical query builder. By establishing and interconnecting an OSSE bridgehead with the underlying ESID registry, confederated queries for data can be received and, if desired, the inquirer can be contacted to further discuss any requirements for cooperation. CONCLUSIONS: The OSSE offers an infrastructure that provides the possibility of more collaborative and transparent research. The decentralized search functionality includes registries into one search application while still maintaining data sovereignty. The OSSE bridgehead enables any registry software to be integrated into the OSSE network. The proposed toolchain to set up the required interfaces consists of freely available software components that are well documented. The use of the decentralized search is uncomplicated to use and offers a well-structured, yet still improvable, graphical user interface to formulate queries.

The Status Quo of Rare Diseases Centres for the Development of a Clinical Decision Support System - A Cross-Sectional Study.

Clinical decision support systems (CDSS) help to improve the diagnostics and treatment of rare diseases (RD). As one of four funded consortia of the Medical Informatics Initiative supported by the Federal Ministry of Education and Research (BMBF, Germany), MIRACUM develops a clinical decision support system (CDSS) for RD based on distributed data of ten university hospitals. The CDSS will be developed at the Rare Diseases Centres (RDC) of the MIRACUM consortium. Since it is essential to deliver decision support at the right time and place in the clinician's workflow, this study aimed to capture relevant information of the RDCs regarding patient admission and diagnostic process. Additionally, we investigated how patient documentation and digitalisation is performed at the centres. Therefore, we conducted a cross-sectional survey involving experts in the RDs domain to capture relevant information for the further development of a CDSS in RD. For each centre, one expert on RDs participated in the study (n=8). The survey identified several challenges regarding the reuse of patient data, e.g. the paper-based documentation of a patientâAZs medical history and coding of diagnoses using ICD-10. However, we noticed a relevant use of current software diagnosis support and a similarly performed diagnostic process in all RDC. Further studies are needed to get more detailed insights and to define specific requirements.

Semantically Annotated Metadata: Interconnecting Samply.MDR and MDM-Portal.

Interoperability is a growing demand in healthcare, caused by heterogeneous sources, which aggravate information transfer. The interoperability issues can be addressed by metadata repositories. These support to ensure syntactical interoperability, like compatible data formats or value ranges, however especially semantic interoperability is still challenging. Semantic annotation through standardized terminologies and classifications enables to foster semantic interoperability. This work aims to interconnect Samply.MDR and Portal of Medical Data Model (MDM-Portal) to allow facilitated semantic annotation with UMLS. Therefore, Samply.MDR was extended to store semantic information. While creating a data element, a request to MDM is send, which results in possible UMLS codes. The user can now adopt the most suitable code and select a link type between the code and the element itself. A successful enrichment of data elements with UMLS codes was shown by interconnecting Samply.MDR and MDM-Portal.

Scientific Challenge in eHealth: MAPPATHON, a Metadata Mapping Challenge.

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.

Finding the Needle in the Hay Stack: An Open Architecture to Support Diagnosis of Undiagnosed Patients.

Clinical Decision Support Systems (CDSS) are promising to support physicians in finding the right diagnosis of patients with rare diseases (RD). The MIRACUM consortium, which includes ten university hospitals in Germany, will establish a diagnosis support system for RD. This system conducts a similarity analysis on distributed clinical data with the aim to identify similar patient cases at each MIRACUM site to offer the physician a hint to a possible diagnosis.

Samply.MDR - A Metadata Repository and Its Application in Various Research Networks.

Collaboration in medical research is becoming common, especially for collecting relevant cases across institutional boundaries. If the data, which is usually very heterogeneously formalized and structured, can be integrated, such a collaboration can facilitate research. An absolute prerequisite for this is an extensive description about the formalization and exact meaning of every data element contained in a dataset. This information is commonly known as metadata. Various research networking projects tackle this challenge with the development of concepts and IT tools. The Samply Metadata Repository (Samply.MDR) is a solution for managing and publishing such metadata in a standardized and reusable way. In this article we present the structure and features of the Samply.MDR as well as its flexible usability by giving an overview about its application in various projects.

OSSE Goes FAIR - Implementation of the FAIR Data Principles for an Open-Source Registry for Rare Diseases.

The Open Source Registry for Rare Diseases (OSSE) provides a concept and a software for the management of registries for patients with rare diseases. A disease is defined as rare if less than 5 out of 10,000 people are affected. Up to date, approximately 6,000 rare diseases are catalogued. Networking and data exchange for research purposes remains challenging due to the paucity of interoperability and due to the fact that small data stocks are stored locally. The so called "Findable, Accessible, Interoperable, Reusable" (FAIR) Data Principles have been developed to improve research in the field of rare diseases. Subsequently, the OSSE architecture was adapted to implement the FAIR Data Principles. Therefore, the so-called FAIR Data Point was integrated into OSSE to provide a description of metadata in a FAIR manner. OSSE relies on the existing metadata repository (MDR), which is used in to define data elements in the system. This is an important step towards unified documentation across multiple registries. The integration and use of new procedures to improve interoperability plays an important role in the context of registries for rare diseases.

On-The-Fly Query Translation Between i2b2 and Samply in the German Biobank Node (GBN) Prototypes.

Information retrieval is a major challenge in medical informatics. Various research projects have worked on this task in recent years on an institutional level by developing tools to integrate and retrieve information. However, when it comes down to querying such data across institutions, the challenge persists due to the high heterogeneity of data and differences in software systems. The German Biobank Node (GBN) project faced this challenge when trying to interconnect four biobanks to enable distributed queries for biospecimens. All biobanks had already established integrated data repositories, and some of them were already part of research networks. Instead of developing another software platform, GBN decided to form a bridge between these. This paper describes and discusses a core component from the GBN project, the OmniQuery library, which was implemented to enable on-the-fly query translation between heterogeneous research infrastructures.

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.

From a Content Delivery Portal to a Knowledge Management System for Standardized Cancer Documentation.

Heterogeneous tumor documentation and its challenges of interpretation of medical terms lead to problems in analyses of data from clinical and epidemiological cancer registries. The objective of this project was to design, implement and improve a national content delivery portal for oncological terms. Data elements of existing handbooks and documentation sources were analyzed, combined and summarized by medical experts of different comprehensive cancer centers. Informatics experts created a generic data model based on an existing metadata repository. In order to establish a national knowledge management system for standardized cancer documentation, a prototypical tumor wiki was designed and implemented. Requirements engineering techniques were applied to optimize this platform. It is targeted to user groups such as documentation officers, physicians and patients. The linkage to other information sources like PubMed and MeSH was realized.