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.

The integration of artificial intelligence (AI) algorithms into clinical practice holds immense potential to improve patient care, but widespread adoption still faces significant challenges, including interoperability issues. We propose a concept for the agile development of an IT platform to integrate AI-based applications into clinical workflows for a use case in ophthalmology.

Introduction Obtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities — including hospitals, outpatient clinics, and physician practices — the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites. Methods We investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term. Results We have developed the pre-built packages “ResearchData-to-FHIR”, “FHIR-to-OMOP” and “Addons”, which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use. Conclusion Our development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

Evidenzbasierte Therapieempfehlungen helfen bei der jeweiligen fachspezifischen Therapie, können aber Daten aus der Real-World-Versorgung kaum berücksichtigen. Um diese im klinischen Alltag auch hinsichtlich prädiktiver Aussagen zu Krankheitsprogression oder Behandlungserfolg besser zu nutzen, sind Modelle mit Daten aus der Versorgung zu entwickeln, um diese für die Schaffung von assistierender künstlicher Intelligenz zu nutzen. Ziel des Use Case 1 innerhalb des MiHUBx (Medical Informatics Hub in Saxony) ist es, ein auf Versorgungs- und Forschungsdaten basierendes Modell für einen Biomarker-gestützten Therapiealgorithmus sowie die dazu notwendige digitale Infrastruktur zu entwickeln. Schrittweise werden notwendige Partner:innen in Kliniken und Praxen technisch oder über Forschungsfragen innerhalb des Use Case 1 „Ophthalmologie trifft Diabetologie“ des regionalen Digitalen FortschrittsHub Gesundheit MiHUBx der bundesweiten Medizininformatik-Initiative zusammengeschlossen. Basierend auf gemeinsamen Studien mit Diabetologen erfolgte die Auswahl robuster serologischer und bildgebender Biomarker, die Hinweise für eine Entwicklung eines diabetischen Makulaödems (DMÖ) geben. Diese und weitere wissenschaftlich nachgewiesene prognostische Marker sollen zukünftig in einen Therapiealgorithmus einfließen, der KI(künstliche Intelligenz)-gestützt ist. Dafür werden gemeinsam mit Medizininformatikern modellhafte Vorgehensweisen erarbeitet sowie ein Datenintegrationszentrum etabliert. Neben der strukturierten und technischen Zusammenführung bisher an verschiedenen Orten vorliegender und teilweise heterogener Versorgungsdaten werden in dem Use Case die Chancen und Hürden zur Nutzung von Real-World-Daten zur Entwicklung künstlicher Intelligenz definiert.

The evaluation of real-world data (RWD) enables insights to be gained from a wide range of patient data collected in routine clinical practice. In addition, multicenter analyses represent a broad and representative patient population and have the potential to capture the actual treatment situation. As a basis for this, the definition of datasets and an infrastructure for data exchange is necessary. Data integration centers (DIC) have already been established at (university) hospitals throughout Germany in order to extract RWD for scientific analyses from the various source systems and integrate them into research-compatible data infrastructures. The project described here aims to demonstrate the added value of this data integration using a case of application in ophthalmology, defining a core dataset as an ophthalmology extension module and establishing a cross-site data exchange infrastructure. As a first step, the treatment success of eye diseases treated with intravitreal injection (IVI) should be improved. To achieve this goal a dashboard for clinical data is provided that clearly visualizes the merged data. Furthermore, algorithms will be developed to identify new imaging biomarkers that can be used for treatment monitoring and predict treatment outcomes.

This study advances the utility of synthetic study data in hematology, particularly for Acute Myeloid Leukemia (AML), by facilitating its integration into healthcare systems and research platforms through standardization into the Observational Medical Outcomes Partnership (OMOP) and Fast Healthcare Interoperability Resources (FHIR) formats. In our previous work, we addressed the need for high-quality patient data and used CTAB-GAN+ and Normalizing Flow (NFlow) to synthesize data from 1606 patients across four multicenter AML clinical trials. We published the generated synthetic cohorts, that accurately replicate the distributions of key demographic, laboratory, molecular, and cytogenetic variables, alongside patient outcomes, demonstrating high fidelity and usability. The conversion to the OMOP format opens avenues for comparative observational multi-center research by enabling seamless combination with related OMOP datasets, thereby broadening the scope of AML research. Similarly, standardization into FHIR facilitates further developments of applications, e.g. via the SMART-on-FHIR platform, offering realistic test data. This effort aims to foster a more collaborative research environment and facilitate the development of innovative tools and applications in AML care and research.

Die Auswertung von Real-World-Daten (RWD) ermöglicht Erkenntnisse aus einer Vielzahl von Patientendaten, die in der klinischen Routine erhoben werden. Multizentrische Analysen bilden darüber hinaus eine breite und repräsentative Patientenpopulation ab und bergen das Potenzial, die reale Versorgungssituation zu erfassen. Als Basis dafür sind die Definition von Datensätzen und eine Infrastruktur zum Datenaustausch notwendig. Datenintegrationszentren (DIZ) sind bereits bundesweit an (universitären) Standorten etabliert worden, um RWD für wissenschaftliche Analysen aus den verschiedenen Quellsystemen zu extrahieren und in forschungskompatiblen Dateninfrastrukturen zu integrieren. Das hier beschriebene Projekt soll den Mehrwert dieser Datenzusammenführung anhand eines Ophthalmologie-Anwendungsfalls demonstrieren und dazu einen Kerndatensatz als Augenheilkunde-Erweiterungsmodul definieren sowie eine standortübergreifende Datenaustauschinfrastruktur etablieren. In einem ersten Schritt soll der Behandlungserfolg bei Augenkrankheiten verbessert werden, die mit intravitrealer operativer Medikamentenapplikation (IVOM) behandelt werden. Zur Erreichung dieses Ziels soll ein Dashboard für klinische Daten bereitgestellt werden, das die zusammengeführten Daten übersichtlich visualisiert. Darüber hinaus sollen Algorithmen zur Identifikation neuer bildgebender Biomarker entwickelt werden, die der Therapieüberwachung dienen und Behandlungsergebnisse prognostizieren können.

BACKGROUND:Evidence-based treatment recommendations are helpful in the corresponding discipline-specific treatment but can hardly take data from real-world care into account. In order to make better use of this in everyday clinical practice, including with respect to predictive statements about disease development or treatment success, models with data from treatment must be developed in order to use them for the development of assistive artificial intelligence. GOAL:The aim of the Use Case 1 of the medical informatics hub in Saxony (MiHUBx) is the development of a model based on treatment and research data for a treatment algorithm supported by biomarkers and also the development of the necessary digital infrastructure. MATERIAL AND METHODS:Step by step, the necessary partners in hospitals and practices will be brought together technically or through research questions within Use Case 1 "Ophthalmology meets Diabetology", a regional digital progress hub in health, the medical informatics hub in Saxony (MiHUBx ) of the nationwide medical informatics initiative (MII). RESULTS:Based on joint studies with diabetologists, robust serological and imaging biomarkers were selected that provide evidence of the development of diabetic macular edema (DME). In the future, these and other scientifically proven prognostic markers will be incorporated into a treatment algorithm that is supported by artificial intelligence (AI). For this purpose, model procedures are being developed together with medical informatics specialists. At the same time, a data integration center (DIZ) was established. CONCLUSION:In addition to the structured and technical combination of the previously disseminated and partially heterogeneous treatment data, the Use Case 1 defines the chances and hurdles for using such real-world data to develop artificial intelligence.

Abstract The EyeMatics project, embedded as a clinical use case in Germany’s Medical Informatics Initiative, is a large digital health initiative in ophthalmology. The objective is to improve the understanding of the treatment effects of intravitreal injections, the most frequent procedure to treat eye diseases. To achieve this, valuable patient data will be meaningfully integrated and visualized from different IT systems and hospital sites. EyeMatics emphasizes a governance framework that actively involves patient representatives, strictly implements interoperability standards, and employs artificial intelligence methods to extract biomarkers from tabular and clinical data as well as raw retinal scans. In this perspective paper, we delineate the strategies for user-centered implementation and health care–based evaluation in a multisite observational technology study.

INTRODUCTION:Seamless interoperability of ophthalmic clinical data is beneficial for improving patient care and advancing research through the integration of data from various sources. Such consolidation increases the amount of data available, leading to more robust statistical analyses, and improving the accuracy and reliability of artificial intelligence models. However, the lack of consistent, harmonized data formats and meanings (syntactic and semantic interoperability) poses a significant challenge in sharing ophthalmic data. METHODS:The Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR), a standard for the exchange of healthcare data, emerges as a promising solution. To facilitate cross-site data exchange in research, the German Medical Informatics Initiative (MII) has developed a core data set (CDS) based on FHIR. RESULTS:This work investigates the suitability of the MII CDS specifications for exchanging ophthalmic clinical data necessary to train and validate a specific machine learning model designed for predicting visual acuity. In interdisciplinary collaborations, we identified and categorized the required ophthalmic clinical data and explored the possibility of its mapping to FHIR using the MII CDS specifications. DISCUSSION:We found that the current FHIR MII CDS specifications do not completely accommodate the ophthalmic clinical data we investigated, indicating that the creation of an extension module is essential.

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.

Background Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. Results In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level. Conclusions By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.