Unlocking Real-World Health Data from Non-University Locations Through Effective Provision of Tools and Knowledge.

INTRODUCTION: The Medical Informatics Initiative (MII) in Germany has pioneered platforms such as the National Portal for Medical Research Data (FDPG) to enhance the accessibility of data from clinical routine care for research across both university and non-university healthcare settings. This study explores the efficacy of the Medical Informatics Hub in Saxony (MiHUBx) services by integrating Klinikum Chemnitz gGmbH (KC) with the FDPG, leveraging the Fast Healthcare Interoperability Resources Core Data Set of the MII to standardize and harmonize data from disparate source systems. METHODS: The employed procedures include deploying installation packages to convert data into FHIR format and utilizing the Research Data Repository for structured data storage and exchange within the clinical infrastructure of KC. RESULT: Our results demonstrate successful integration, the development of a comprehensive deployment diagram, additionally, it was demonstrated that the non-university site can report clinical data to the FDPG. DISCUSSION: The discussion reflects on the practical application of this integration, highlighting its potential scalability to even smaller healthcare facilities and to pave the way to access to more medical data for research. This exemplary demonstration of the interplay of different tools provides valuable insights into technical and operational challenges, setting a precedent for future expansions and contributing to the democratization of medical data access.

Evaluating and Enhancing the Fitness-for-Purpose of Electronic Health Record Data: Qualitative Study on Current Practices and Pathway to an Automated Approach Within the Medical Informatics for Research and Care in University Medicine Consortium.

BACKGROUND: Leveraging electronic health record (EHR) data for clinical or research purposes heavily depends on data fitness. However, there is a lack of standardized frameworks to evaluate EHR data suitability, leading to inconsistent quality in data use projects (DUPs). This research focuses on the Medical Informatics for Research and Care in University Medicine (MIRACUM) Data Integration Centers (DICs) and examines empirical practices on assessing and automating the fitness-for-purpose of clinical data in German DIC settings. OBJECTIVE: The study aims (1) to capture and discuss how MIRACUM DICs evaluate and enhance the fitness-for-purpose of observational health care data and examine the alignment with existing recommendations and (2) to identify the requirements for designing and implementing a computer-assisted solution to evaluate EHR data fitness within MIRACUM DICs. METHODS: A qualitative approach was followed using an open-ended survey across DICs of 10 German university hospitals affiliated with MIRACUM. Data were analyzed using thematic analysis following an inductive qualitative method. RESULTS: All 10 MIRACUM DICs participated, with 17 participants revealing various approaches to assessing data fitness, including the 4-eyes principle and data consistency checks such as cross-system data value comparison. Common practices included a DUP-related feedback loop on data fitness and using self-designed dashboards for monitoring. Most experts had a computer science background and a master's degree, suggesting strong technological proficiency but potentially lacking clinical or statistical expertise. Nine key requirements for a computer-assisted solution were identified, including flexibility, understandability, extendibility, and practicability. Participants used heterogeneous data repositories for evaluating data quality criteria and practical strategies to communicate with research and clinical teams. CONCLUSIONS: The study identifies gaps between current practices in MIRACUM DICs and existing recommendations, offering insights into the complexities of assessing and reporting clinical data fitness. Additionally, a tripartite modular framework for fitness-for-purpose assessment was introduced to streamline the forthcoming implementation. It provides valuable input for developing and integrating an automated solution across multiple locations. This may include statistical comparisons to advanced machine learning algorithms for operationalizing frameworks such as the 3×3 data quality assessment framework. These findings provide foundational evidence for future design and implementation studies to enhance data quality assessments for specific DUPs in observational health care settings.

Leveraging Clinical Data Treasures: Integration of an AI Platform into Clinical IT.

In recent years, there has been a rapid growth in the use of AI in the clinical domain. In order to keep pace with this development, a framework should be created in which clinical AI models can be easily trained, managed and applied. In our study, we propose a clinical AI platform that supports the development cycle and application of clinical AI models. We consider not only the development of an isolated clinical AI platform, but also its integration into clinical IT. This includes the consideration of so-called medical data integration centers. We evaluate our approach with the aid of a clinical AI use case to demonstrate the functionality of our clinical AI platform.

[Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative]. / Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative.

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.

Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study.

BACKGROUND: Secondary investigations into digital health records, including electronic patient data from German medical data integration centers (DICs), pave the way for enhanced future patient care. However, only limited information is captured regarding the integrity, traceability, and quality of the (sensitive) data elements. This lack of detail diminishes trust in the validity of the collected data. From a technical standpoint, adhering to the widely accepted FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship necessitates enriching data with provenance-related metadata. Provenance offers insights into the readiness for the reuse of a data element and serves as a supplier of data governance. OBJECTIVE: The primary goal of this study is to augment the reusability of clinical routine data within a medical DIC for secondary utilization in clinical research. Our aim is to establish provenance traces that underpin the status of data integrity, reliability, and consequently, trust in electronic health records, thereby enhancing the accountability of the medical DIC. We present the implementation of a proof-of-concept provenance library integrating international standards as an initial step. METHODS: We adhered to a customized road map for a provenance framework, and examined the data integration steps across the ETL (extract, transform, and load) phases. Following a maturity model, we derived requirements for a provenance library. Using this research approach, we formulated a provenance model with associated metadata and implemented a proof-of-concept provenance class. Furthermore, we seamlessly incorporated the internationally recognized Word Wide Web Consortium (W3C) provenance standard, aligned the resultant provenance records with the interoperable health care standard Fast Healthcare Interoperability Resources, and presented them in various representation formats. Ultimately, we conducted a thorough assessment of provenance trace measurements. RESULTS: This study marks the inaugural implementation of integrated provenance traces at the data element level within a German medical DIC. We devised and executed a practical method that synergizes the robustness of quality- and health standard-guided (meta)data management practices. Our measurements indicate commendable pipeline execution times, attaining notable levels of accuracy and reliability in processing clinical routine data, thereby ensuring accountability in the medical DIC. These findings should inspire the development of additional tools aimed at providing evidence-based and reliable electronic health record services for secondary use. CONCLUSIONS: The research method outlined for the proof-of-concept provenance class has been crafted to promote effective and reliable core data management practices. It aims to enhance biomedical data by imbuing it with meaningful provenance, thereby bolstering the benefits for both research and society. Additionally, it facilitates the streamlined reuse of biomedical data. As a result, the system mitigates risks, as data analysis without knowledge of the origin and quality of all data elements is rendered futile. While the approach was initially developed for the medical DIC use case, these principles can be universally applied throughout the scientific domain.

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.

FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital.

BACKGROUND: Secondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This so-called "real world data" are essential to complement knowledge and results from clinical trials. Furthermore, big data may help in establishing precision medicine. However, manual data extraction and annotation workflows to transfer routine data into research data would be complex and inefficient. Generally, best practices for managing research data focus on data output rather than the entire data journey from primary sources to analysis. To eventually make routinely collected data usable and available for research, many hurdles have to be overcome. In this work, we present the implementation of an automated framework for timely processing of clinical care data including free texts and genetic data (non-structured data) and centralized storage as Findable, Accessible, Interoperable, Reusable (FAIR) research data in a maximum care university hospital. METHODS: We identify data processing workflows necessary to operate a medical research data service unit in a maximum care hospital. We decompose structurally equal tasks into elementary sub-processes and propose a framework for general data processing. We base our processes on open-source software-components and, where necessary, custom-built generic tools. RESULTS: We demonstrate the application of our proposed framework in practice by describing its use in our Medical Data Integration Center (MeDIC). Our microservices-based and fully open-source data processing automation framework incorporates a complete recording of data management and manipulation activities. The prototype implementation also includes a metadata schema for data provenance and a process validation concept. All requirements of a MeDIC are orchestrated within the proposed framework: Data input from many heterogeneous sources, pseudonymization and harmonization, integration in a data warehouse and finally possibilities for extraction or aggregation of data for research purposes according to data protection requirements. CONCLUSION: Though the framework is not a panacea for bringing routine-based research data into compliance with FAIR principles, it provides a much-needed possibility to process data in a fully automated, traceable, and reproducible manner.

Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.