Search

Planning clinical studies to check medical hypotheses requires the specification of eligibility criteria in order to identify potential study participants. Electronically available patient data allows to support the recruitment of patients for studies. The Smart Medical Information Technology for Healthcare (SMITH) consortium aims to establish data integration centres to enable the innovative use of available healthcare data for research and treatment optimization. The data from the electronic health record of patients in the participating hospitals is integrated into a Health Data Storage based on the Fast Healthcare Interoperability Resources standard (FHIR), developed by HL7. In SMITH, FHIR Search is used to query the integrated data. An investigation has shown the advantages and disadvantages of using FHIR Search for specifying eligibility criteria. This paper presents an approach for modelling eligibility criteria as well as for generating and executing FHIR Search queries. Our solution is based on the Phenotype Manager, a general ontological phenotyping framework to model and calculate phenotypes using the Core Ontology of Phenotypes.

Introduction: To support research projects that require medical data from multiple sites is one of the goals of the German Medical Informatics Initiative (MII). The data integration centers (DIC) at university medical centers in Germany provide patient data via FHIR® in compliance with the MII core data set (CDS). Requirements for data protection and other legal bases for processing prefer decentralized processing of the relevant data in the DICs and the subsequent exchange of aggregated results for cross-site evaluation. Methods: Requirements from clinical experts were obtained in the context of the MII use case INTERPOLAR. A software architecture was then developed, modeled using 3LGM², finally implemented and published in a github repository. Results: With the CDS tool chain, we have created software components for decentralized processing on the basis of the MII CDS. The CDS tool chain requires access to a local FHIR endpoint and then transfers the data to an SQL database. This is accessed by the DataProcessor component, which performs calculations with the help of rules (input repo) and writes the results back to the database. The CDS tool chain also has a frontend module (REDCap), which is used to display the output data and calculated results, and allows verification, evaluation, comments and other responses. This feedback is also persisted in the database and is available for further use, analysis or data sharing in the future. Discussion: Other solutions are conceivable. Our solution utilizes the advantages of an SQL database. This enables flexible and direct processing of the stored data using established analysis methods. Due to the modularization, adjustments can be made so that it can be used in other projects. We are planning further developments to support pseudonymization and data sharing. Initial experience is being gathered. An evaluation is pending and planned. [ABSTRACT FROM AUTHOR]

Introduction NFDI4Health is a consortium funded by the German Research Foundation to make structured health data findable and accessible internationally according to the FAIR principles. Its goal is bringing data users and Data Holding Organizations (DHOs) together. It mainly considers DHOs conducting epidemiological and public health studies or clinical trials. Methods Local data hubs (LDH) are provided for such DHOs to connect decentralized local research data management within their organizations with the option of publishing shareable metadata via centralized NFDI4Health services such as the German central Health Study Hub. The LDH platform is based on FAIRDOM SEEK and provides a complete and flexible, locally controlled data and information management platform for health research data. A tailored NFDI4Health metadata schema for studies and their corresponding resources has been developed which is fully supported by the LDH software, e.g. for metadata transfer to other NFDI4Health services. Results The SEEK platform has been technically enhanced to support extended metadata structures tailored to the needs of the user communities in addition to the existing metadata structuring of SEEK. Conclusion With the LDH and the MDS, the NFDI4Health provides all DHOs with a standardized and free and open source research data management platform for the FAIR exchange of structured health data. [ABSTRACT FROM AUTHOR]

Introduction The Local Data Hub (LDH) is a platform for FAIR sharing of medical research (meta-)data. In order to promote the usage of LDH in different research communities, it is important to understand the domain-specific needs, solutions currently used for data organization and provide support for seamless uploads to a LDH. In this work, we analyze the use case of microneurography, which is an electrophysiological technique for analyzing neural activity. Methods After performing a requirements analysis in dialogue with microneurography researchers, we propose a concept-mapping and a workflow, for the researchers to transform and upload their metadata. Further, we implemented a semi-automatic upload extension to odMLtables, a template-based tool for handling metadata in the electrophysiological community. Results The open-source implementation enables the odML-to-LDH concept mapping, allows data anonymization from within the tool and the creation of custom-made summaries on the underlying data sets. Discussion This concludes a first step towards integrating improved FAIR processes into the research laboratory's daily workflow. In future work, we will extend this approach to other use cases to disseminate the usage of LDHs in a larger research community. [ABSTRACT FROM AUTHOR]

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The German Medical Informatics Initiative has agreed on a HL7 FHIR-based core data set as the common data model that all 37 university hospitals use for their patient's data. These data are stored locally at the site but are centrally queryable for researchers and accessible upon request. This infrastructure is currently under construction, and its functionality is being tested by so-called Projectathons. In the 6th Projectathon, a clinical hypothesis was formulated, executed in a multicenter scenario, and its results were analyzed. A number of oddities emerged in the analysis of data from different sites. Biometricians, who had previously performed analyses in prospective data collection settings such as clinical trials or cohorts, were not consistently aware of these idiosyncrasies. This field report describes data quality problems that have occurred, although not all are genuine errors. The aim is to point out such circumstances of data generation that may affect statistical analysis. [ABSTRACT FROM AUTHOR]

Introduction: Prospective data collection in clinical trials is considered the gold standard of clinical research. Validating data entered in input fields in case report forms is unavoidable to maintain good data quality. Data quality checks include both the conformance of individual inputs to the specification of the data element, the detection of missing values, and the plausibility of the values entered. State-of-the-Art: Besides Libre-/OpenClinica there are many applications for capturing clinical data. While most of them have a commercial approach, free and open-source solutions lack intuitive operation. Concept: Our ocRuleTool is made for the specific use case to write validation rules for Open-/LibreClinica, a clinical study management software for designing case report forms and managing medical data in clinical trials. It addresses parts of all three categories of data quality checks mentioned above. Implementation: The required rules and error messages are entered in the normative Excel specification and then converted to an XML document which can be uploaded to Open-/LibreClinica. The advantage of this intermediate step is a better readability as the complex XML elements are broken down into easy to fill out columns in Excel. The tool then generates the ready to use XML file by itself. Lessons Learned: This approach saves time, is less error-prone and allows collaboration with clinicians on improving data quality. Conclusion: Our ocRuleTool has proven useful in over a dozen studies. We hope to increase the user base by releasing it to open source on GitHub. [ABSTRACT FROM AUTHOR]

Background Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. Objective The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. Methods Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. Results The LHA platform is in productive mode (). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany. [ABSTRACT FROM AUTHOR]

Sharing data is of great importance for research in medical sciences. It is the basis for reproducibility and reuse of already generated outcomes in new projects and in new contexts. FAIR data principles are the basics for sharing data. The Leipzig Health Atlas (LHA) platform follows these principles and provides data, describing metadata, and models that have been implemented in novel software tools and are available as demonstrators. LHA reuses and extends three different major components that have been previously developed by other projects. The SEEK management platform is the foundation providing a repository for archiving, presenting and secure sharing a wide range of publication results, such as published reports, (bio)medical data as well as interactive models and tools. The LHA Data Portal manages study metadata and data allowing to search for data of interest. Finally, PhenoMan is an ontological framework for phenotype modelling. This paper describes the interrelation of these three components. In particular, we use the PhenoMan to, firstly, model and represent phenotypes within the LHA platform. Then, secondly, the ontological phenotype representation can be used to generate search queries that are executed by the LHA Data Portal. The PhenoMan generates the queries in a novel domain specific query language (SDQL), which is specific for data management systems based on CDISC ODM standard, such as the LHA Data Portal. Our approach was successfully applied to represent phenotypes in the Leipzig Health Atlas with the possibility to execute corresponding queries within the LHA Data Portal. [ABSTRACT FROM AUTHOR]

Background: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term 'phenotype' has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case 'phenotype pipeline' (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies. [ABSTRACT FROM AUTHOR]

Despite their young age, the FAIR principles are recognised as important guidelines for research data management. Their generic design, however, leaves much room for interpretation in domain-specific application. Based on practical experience in the operation of a data repository, this article addresses problems in FAIR provisioning of medical data for research purposes in the use case of the Leipzig Health Atlas project and shows necessary future developments. [ABSTRACT FROM AUTHOR]

Phenotyping means the determination of clinical relevant phenotypes, e.g. by classification or calculation based on EHR data. Within the German Medical Informatics Initiative, the SMITH consortium is working on the implementation of a phenotyping pipeline. to extract, structure and normalize information from the EHR data of the hospital information systems of the participating sites; to automatically apply complex algorithms and models and to enrich the data within the research data warehouses of the distributed data integration centers with the computed results. Here we present the overall picture and essential building blocks and workflows of this concept. [ABSTRACT FROM AUTHOR]

Clinical Data Management Systems (CDMS) are used to electronically capture and store data about study participants in clinical trials. CDMS tend to be superior compared to paper-based data capture with respect to data quality, consistency, completeness and traceability. Nevertheless, their application is not default - especially in small-scale, academic clinical studies. While clinical researchers can choose from many different software vendors, the vast requirements of data management and the growing need for integration with other systems make it hard to select the most suitable one. Additionally, the financial and personnel costs for purchasing, deploying and maintaining a commercial solution can easily go beyond the limits of a research project's resources. The aim of this paper is to assess the suitability of the web-based open-source software OpenClinica for academic clinical trials with regards to functionalities required in a large research network. [ABSTRACT FROM AUTHOR]

The largest publicly funded project to generate a German-language medical text corpus will start in mid-2023. GeMTeX comprises clinical texts from information systems of six university hospitals, which will be made accessible for NLP by annotation of entities and relations, which will be enhanced with additional meta-information. A strong governance provides a stable legal framework for the use of the corpus. State-of-the art NLP methods are used to build, pre-annotate and annotate the corpus and train language models. A community will be built around GeMTeX to ensure its sustainable maintenance, use, and dissemination. [ABSTRACT FROM AUTHOR]

Medical research is an active field in which a wide range of information is collected, collated, combined and analyzed. Essential results are reported in publications, but it is often problematic to have the data (raw and processed), algorithms and tools associated with the publication available. The Leipzig Health Atlas (LHA) project has therefore set itself the goal of providing a repository for this purpose and enabling controlled access to it via a web-based portal. A data sharing concept in accordance to FAIR and OAIS is the basis for the processing and provision of data in the LHA. An IT architecture has been designed for this purpose. The paper presents essential aspects of the data sharing concept, the IT architecture and the methods used. [ABSTRACT FROM AUTHOR]

We present a working approach for a clinical research database as part of an archival information system. The CDISC ODM standard is target for clinical study and research relevant routine data, thus decoupling the data ingest process from the access layer. The presented research database is comprehensive as it covers annotating, mapping and curation of poorly annotated source data. Besides a conventional relational database the medical data warehouse i2b2 serves as main frontend for end-users. The system we developed is suitable to support patient recruitment, cohort identification and quality assurance in daily routine. [ABSTRACT FROM AUTHOR]