How to Make Outpatient Healthcare Data in Germany Available for Research in the Dynamic Course of Digital Transformation.

INTRODUCTION: There is increasing interest on re-use of outpatient healthcare data for research, as most medical diagnosis and treatment is provided in the ambulatory sector. One of the early projects to bring primary data from German ambulatory care into clinical research technically, organizationally and in compliance with legal demands has been the RADAR project, that is based on a broad consent and has used the then available practice information system's interfaces to extract and transfer data to a research repository. In course of the digital transformation of the German healthcare system, former standards are abandoned and new interoperability standards, interfaces and regulations on secondary use of patient data are defined, however with slow adoption by Health-IT systems. Therefore, it is of importance for all initiatives that aim at using ambulatory healthcare data for research, how to access this data in an efficient and effective way. METHODS: Currently defined healthcare standards are compared regarding coverage of data relevant for research as defined by the RADAR project. We compare four architectural options to access ambulatory health data through different components of healthcare and health research data infrastructures along the technical, organizational and regulatory conditions, the timetable of dissemination and the researcher's perspective. RESULTS: A high-level comparison showed a high degree of semantic overlap in the information models used. Electronic patient records and practice information systems are alternative data sources for ambulatory health data - but differ strongly in data richness and accessibility. CONCLUSION: Considering the compared dimensions of architectural routes to access health data for secondary research use we conclude that data extraction from practice information systems is currently the most promising way due to data availability on a mid-term perspective. Integration of routine data into the national research data infrastructures might be enforced by convergence of to date different information models.

Towards a Recommendation for Good Health Data Modeling (GHDM) - Results of Expert Interviews.

Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories "governance", "modeling" and "standards", the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.

Towards a Didactic Concept for Heterogeneous Target Groups in Digital Learning Environments-First Course Implementation.

In the context of the ongoing digitization of interdisciplinary subjects, the need for digital literacy is increasing in all areas of everyday life. Furthermore, communication between science and society is facing new challenges, not least since the COVID-19 pandemic. In order to deal with these challenges and to provide target-oriented online teaching, new educational concepts for the transfer of knowledge to society are necessary. In the transfer project "Zukunftslabor Gesundheit" (ZLG), a didactic concept for the creation of E-Learning classes was developed. A key factor for the didactic concept is addressing heterogeneous target groups to reach the broadest possible spectrum of participants. The concept has already been used for the creation of the first ZLG E-Learning courses. This article outlines the central elements of the developed didactic concept and addresses the creation of the ZLG courses. The courses created so far appeal to different target groups and convey diverse types of knowledge at different levels of difficulty.

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.

Development of a Didactic Online Course Concept for Heterogeneous Audience Groups in the Context of Healthcare IT.

Building a well-founded understanding of the concepts, tasks and limitations of IT in all areas of society is an essential prerequisite for future developments in business and research. This applies in particular to the healthcare sector and medical research, which are affected by the noticeable advances in digitization. In the transfer project "Zukunftslabor Gesundheit" (ZLG), a teaching framework was developed to support the development of further education online courses in order to teach heterogeneous groups of learners independent of location and prior knowledge. The study at hand describes the development and components of the framework.

Evaluation of a Clinical Decision Support System in the Domain of Chronic Wound Management.

The PosiThera project focuses on the management of chronic wounds, which is multi-professional and multi-disciplinary. For this context, a software prototype was developed in the project, which is intended to support medical and nursing staff with the assistance of artificial intelligence. In accordance with the user-centred design, national workshops were held at the beginning of the project with the involvement of domain experts in wound care in order to identify requirements and use cases of IT systems in wound care, with a focus on AI. In this study, the focus was on involving nursing and nursing science staff in testing the software prototype to gain insights into its functionality and usability. The overarching goal of the iterative testing and adaptation process is to further develop the prototype in a way that is close to care.

Illustration of Clinical Decision Support System Development Complexity.

The dissemination of Clinical Decision Support Systems (CDSS) in the medical field is slow due to various reasons such as lacking comprehensibility, low user acceptance, specific problem settings and unique application environments. This paper presents an illustration of the complexity of the CDS development process. Guided by procedural software development models already known from the field of software engineering, we developed a CDS-specific software lifecycle and a CDS development complexity illustration. We based the results on literature research of publicated field reports about successfully developed CDSS. We identified important CDS peculiarities related to the development and documentation process of CDSS and later merged them with generic software engineering models. We then created a CDS complexity illustration that can be used to structure future research in the CDS field as well as any standardisation processes.

Implementation and Analysis of Two Knowledge Base Approaches for the Treatment of Chronic Wounds.

The access to data in healthcare is an enabler for the implementation of clinical decision support systems (CDSS) in practice. The usage of CDSS aims to be of efficient assistance to healthcare providers. The aim of the BMBF project "PosiThera", is to support the involved professions in the treatment process of chronic wounds. In this study we implemented the formalized knowledge of chronic wound diagnosis into two different knowledge base approaches, the HL7 Arden Syntax and a Petri net approach. The motivating factor behind our study was to use both approaches for the implementation of the projects knowledge base and to compare the results. We implemented the formalized knowledge successfully in both approaches. The results of our comparison showed similarities and differences of the Arden Syntax and the Petri net approach, which might support the evolution of both approaches in the future.