[MiHUBx: a digital progress hub for the use of intersectoral clinical data sets using the example of diabetic macular edema].

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.

From evidence to care delivery: Opportunities and risks in health and science policy. A position paper of the Association of the Scientific Medical Societies in Germany (AWMF).

Objective: After over 25 years of developing clinical practice guidelines, the Association of the Scientific Medical Societies in Germany (AWMF) held a symposium to discuss the following topics in order to improve the way evidence is implemented in the delivery of care: expansion of the data pool for guideline development, the regulatory policy framework for this expansion, the transfer of clinical practice guideline statements to medical practice, the associated opportunities and risks resulting from the European legislation.

Methods: The AWMF held its Berlin Forum on 27 April 2022 where experts from scientific medical societies and national institutions in the healthcare sector reported their experiences and perceptions on the topics mentioned. Three writing groups compiled the key statements from these contributions to and discussions made at the Berlin Forum into a position paper.

Horse Diagnosis and Triage Accuracy of GPT-4o.

Animal owners may increasingly rely on large language models for gathering animal health information alongside internet sources in the future. This study therefore aims to provide initial results on the accuracy of ChatGPT-4o in triage and tentative diagnostics, using horses as a case study. Ten test vignettes were used to prompt situation assessments from the tool, which were then compared to original assessments made by a veterinary specialist for horses.

Digitalisation level, strategies and interoperable standards in the nursing care sector in Germany and in international contexts: a study protocol for a systematic review.

Introduction: The nursing care sector faces significant challenges due to an ageing population and a concurrent increase in demand for care services. Digitalisation can be one way of overcoming these challenges by optimising care processes, such as streamlining documentation procedures. However, Germany remains in the early stages of digitalisation in nursing care.

[Access to and use of Data for better Healthcare: A Plea for a cooperative data and Research Infrastructure of Statutory and Private Health Insurers and the Network University Medicine (NUM)].

With the Network of University Medicine (NUM) and the Medical Informatics Initiative (MII), the BMBF is funding two pioneering, structure-building research measures that are now being merged. The data integration centers (DIZ) of the MII are to be consolidated in the NUM. The aim is to establish a standardized research infrastructure within which the existing data from the clinical routine care of the 36 German university hospitals, from clinical cohorts and clinical-epidemiological studies can be used for various research questions upon request and via coordinated processes.

Designing an mHealth Application to Support Horse Owners in Assessing Their Horse's Health Status - Results of a Usability Test.

Introduction: Existing research agrees that a well-thought design of the user interface is a key point for an mHealth application for animal owners, supporting them obtain information and make decisions regarding their pet's specific situation. However, there is currently a lack of specific advice on the design of such an application.

Methods: As part of a user-centered design (UCD) process, a formative, explorative usability test with n = 5 users was conducted for collecting design ideas.

Visualization Techniques for Summarizing Single Patient Health Data to Support Physicians' Clinical Decisions - A Scoping Review.

Introduction: User-centered data visualizations can reduce physician cognitive load and support clinical decision making. To facilitate the selection of appropriate visualizations for single patient health data summaries, this scoping review provides a literature overview of possible visualization techniques and the corresponding reported user-centered design phases.

Methods: The publication databases PubMed, Web of Science, IEEE Xplore and ACM Digital Library were searched for relevant articles from 2017 to 2022.

Challenges in Retrieving Patterns from Generic Data Structures in Clinical Systems - A Technical Case Report.

Introduction: The secondary use of data in clinical environments offers significant opportunities to enhance medical research and practices. However, extracting data from generic data structures, particularly the Entity-Attribute-Value (EAV) model, remains challenging. This study addresses these challenges by developing a methodological approach to convert EAV-based data into a format more suitable for analysis.

Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care.

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.

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.

A Concept for Integrating AI-Based Support Systems into Clinical Practice.

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.

User-Centered Development of Explanation User Interfaces for AI-Based CDSS: Lessons Learned from Early Phases.

This paper reports lessons learned during the early phases of the user-centered design process for an explanation user interface for an AI-based clinical decision support system for the intensive care unit. This paper focuses on identifying and verifying physicians' explanation needs in a multi-center, multi-country project. The explanation needs identified through context analysis and user requirements prioritization in an initial center differed from those identified through questionnaire responses from N= 9 physicians after a multi-center project workshop.

Enhancing Patient Care: Evaluating Clinical Utility in Measurement Instruments.

The evaluation of clinical utility is essential for the successful adoption of new technology in clinical practice. An approach to evaluating clinical utility is presented here using the example of digitized measurement instruments.

Mapping Medical Context: Workshop-Driven Clustering of Influencing Factors on Medical Decision-Making.

Clinical decision support systems (CDSS) can efficiently support doctors in coping with ever-increasing amounts of data by providing evidence-based recommendations for medical decisions. To integrate the systems into the medical workflow and provide patient-specific recommendations for action in the context of personalized medicine, it is essential to tailor the systems to the context of use. This study aims to present an overview of factors influencing medical decision-making that CDSS must consider.

Towards Efficient Resource Utilization Forecasting in Acute Heat Events Using OMOP.

Predicting resource utilization can help to optimize the distribution of limited resources in the healthcare system. This requires different climatic and medical data from different sources, which can lead to problems with interoperability. In the paper we describe which data is needed for the prediction and how the data can be made interoperable using OMOP CDM.

Synthetic Data Generation in Hematology - Paving the Way for OMOP and FHIR Integration.

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.

How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

Background: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases.

Secondary use of patient data within decentralized studies using the example of rare diseases in Germany: A data scientist's exploration of process and lessons learned.

Objective: Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance.

Evaluation of a Digital Decision Aid for Knee Replacement Surgery.

Background: We studied whether an individualized digital decision aid can improve decision-making quality for or against knee arthroplasty.

Methods: An app-based decision aid (EKIT tool) was developed and studied in a stepped-wedge, cluster-randomized trial. Consecutive patients with knee osteoarthritis who were candidates for knee replacement were included in 10 centers in Germany.

State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.

Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was trained and evaluated on 3423 polysomnograms of people with various sleep disorders. The model architecture is a U-net that accepts 50 Hz signals as input.

Streamlining intersectoral provision of real-world health data: a service platform for improved clinical research and patient care.

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.

Transition database for rare diseases and its use for clinical documentation.

Patients with rare diseases commonly suffer from severe symptoms as well as chronic and sometimes life-threatening effects. Not only the rarity of the diseases but also the poor documentation of rare diseases often leads to an immense delay in diagnosis. One of the main problems here is the inadequate coding with common classifications such as the International Statistical Classification of Diseases and Related Health Problems.