Ann-Kristin Kock-Schoppenhauer

Background: HL7 FHIR terminological services (TS) are a valuable tool towards better healthcare interoperability, but require representations of terminologies using FHIR resources to provide their services. As most terminologies are not natively distributed using FHIR resources, converters are needed. Large-scale FHIR projects, especially those with a national or even an international scope, define enormous numbers of value sets and reference many large and complex code systems, which must be regularly updated in TS and other systems. This necessitates a flexible, scalable and efficient provision of these artifacts. This work aims to develop a comprehensive, extensible and accessible toolkit for FHIR terminology conversion, making it possible for terminology authors, FHIR profilers and other actors to provide standardized TS for large-scale terminological artifacts. Implementation: Based on the prevalent HL7 FHIR Shorthand (FSH) specification, a converter toolkit, called BabelFSH , was created that utilizes an adaptable plugin architecture to separate the definition of content from that of the needed declarative metadata. The development process was guided by formalized design goals. Results: All eight design goals were addressed by BabelFSH. Validation of the systems’ performance and completeness was exemplarily demonstrated using Alpha-ID-SE, an important terminology used for diagnosis coding especially of rare diseases within Germany. The tool is now used extensively within the content delivery pipeline for a central FHIR TS with a national scope within the German Medical Informatics Initiative and Network University Medicine and demonstrates adequate usability for FHIR developers. Discussion: The first development focus was geared towards the requirements of the central research FHIR TS for the federated FHIR infrastructure in Germany, and has proven to be very useful towards that goal. Opportunities for further improvement were identified in the validation process especially, as the validation messages are currently imprecise at times. The design of the application lends itself to the implementation of further use cases, such as direct connectivity to legacy systems for catalog conversion to FHIR. Conclusions: The developed BabelFSH tool is a novel, powerful and open-source approach to making heterogenous sources of terminological knowledge accessible as FHIR resources, thus aiding semantic interoperability in healthcare in general.

In the evolving landscape of health care, data use plays an ever-increasing role in health care IT. However, data are often siloed and uncoded free text distributed across several IT systems. This paper introduces a health knowledge management platform, designed to integrate, harmonize, and enable reuse of health care and medical research data. The platform aims to bridge the gap between research and patient care, showcased through real-world scenarios, emphasizing data harmonization and knowledge management within a health care institution. The study is based at the University Hospital Schleswig-Holstein. The main objective of this project is to design, implement, and evaluate a knowledge management platform that integrates health care and biomedical research to support use cases in both domains. The study describes the "health knowledge management platform" designed to access and gain knowledge from health care and medical research data. We performed several rounds of focus groups with stakeholders to elicit the platform requirements. In the process, we identified key aspects of the platform. From the functional requirements, we designed an architectural concept. The platform evaluation follows the Framework for Evaluation in Design Science Research and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 standard with a focus on key aspects identified and real-world scenarios. Two application scenarios, cardiology and radiology, are selected for a requirement-based, qualitative evaluation. We show that our health knowledge management platform is capable of integrating diverse data formats like Health Level 7 Version 2 messages, CSV exports, and Digital Imaging and Communications in Medicine. It currently integrates over 46 million admit, discharge, transfer messages, 38 million imaging studies, and structured clinical data for approximately 1.5 million patients. The platform supports different scenarios based on its 5-layer architecture, including a clinical data repository and services like Master Patient Index and Consent Management. The evaluation against 39 predefined functional requirements showed our platform's capability in certain real-world scenarios of cardiology and radiology. Our evaluation demonstrates that the platform covers the majority of the identified requirements to support knowledge management in health care institutions. Our requirement-based evaluation of the health knowledge management platform at University Hospital Schleswig-Holstein reveals its capabilities, which is possibly leading to better knowledge transfer between patient care and research. The platform's architecture and standardized data improve the quality of data and facilitate access to knowledge. Ongoing development and potential quantitative measures will further enhance its applicability in dynamic health care landscapes.

BACKGROUND In the evolving landscape of healthcare, data utilization plays an ever-increasing role in health care IT. However, data are often siloed, and uncoded free-text, distributed across several IT systems. This paper introduces a Health Knowledge Management Platform, designed to integrate, harmonize and enable re-use of health care and medical research data. The platform aims to bridge the gap between research and patient care, showcased through real-world scenarios, emphasizing data harmonization and knowledge management within a healthcare institution. The study is based at University Hospital Schleswig-Holstein's (UKSH). OBJECTIVE The main objective of this project is to design, implement and evaluate a knowledge management platform that integrates health care and biomedical research to support use cases in both domains. METHODS The study describes the " health knowledge management platform" designed to access and gain knowledge from health care and medical research data. We performed several rounds of focus groups with stakeholders to elicit the platform requirements. In the process we identified key aspects of the platform. From the requirements we designed an architecture concept. The platform evaluation follows the Framework for Evaluation in Design Science Research (FEDS) and ISO/IEC 25000 standard with a focus on key aspects identified, and real-world scenarios. Two application scenarios – cardiology and radiology – are selected for a naturalistic, qualitative evaluation. RESULTS We show that our Open Health Knowledge Management Platform is capable of integrating diverse data formats like HL7® V2 messages, CSV exports, and DICOM® imaging data. The platform is also capable of supporting different scenarios based on its five-layer architecture including a clinical data repository and services like Master Patient Index and Consent Management. The evaluation showed our platform’s capability in certain real-world scenarios of cardiology and radiology. Our evaluation confirms the platform’s coverage of key points and requirements identified to support knowledge management in health care institutions. CONCLUSIONS Our evaluation of the health knowledge management platform at UKSH reveals its capabilities which are possibly leading to better knowledge transfer between patient care and research. The platform's architecture and standardized data improve the quality of data and facilitates access to knowledge. Ongoing development and potential quantitative measures will further enhance its applicability and performance in dynamic health care landscapes. CLINICALTRIAL N/A

Background: Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). Objective: In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. Methods: FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. Results: The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians’ specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. Conclusions: The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology.

This paper explores the challenges and lessons learned during the mapping of HL7 v2 messages structured using custom schema to openEHR for the Medical Data Integration Center (MeDIC) of the University Hospital, Schleswig-Holstein (UKSH). Missing timestamps in observations, missing units of measurement, inconsistencies in decimal separators and unexpected datatypes were identified as critical inconsistencies in this process. These anomalies highlight the difficulty of automating the transformation of HL7 v2 data to any standard, particularly openEHR, using off-the-shelf tools. Addressing these anomalies is crucial for enhancing data interoperability, supporting evidence-based research, and optimizing clinical decision-making. Implementing proper data quality measures and governance will unlock the potential of integrated clinical data, empowering clinicians and researchers and fostering a robust healthcare ecosystem.

BACKGROUND Reaching a significant amount of interoperability between proprietary healthcare systems is an ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from source to target systems. The Mirth Connect Server, which is an open-source communication server, offers in addition to the exchange functionality, also functions for the simultaneous manipulation of data. In recent times, the standard Fast Healthcare Interoperability Resources (FHIR) is becoming more and more prevalent in the German healthcare system. This standard specifies its own standardized mechanisms for transforming data structures in the form of StructureMaps and the FHIR Mapping Language. OBJECTIVE In this study, a generic approach will be developed, which allows to apply these formalized mapping rules defined by the FHIR Mapping Language in an exchangeable manner. A transformation engine is required to execute the mapping rules. METHODS FHIR natively defines resources to support conversion of instance data, the FHIR resource StructureMap. This resource encodes all information required to transform data from a source system into a target system. In our approach, this information is defined in an implementation-independent manner using the FHIR Mapping Language. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in form of JavaScript. These channels can be deployed to the Mirth Connect Server. RESULTS The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from a given mapping based on the underlying StructureMaps that contain the detailed rules. The implementation of the translate functionality is guaranteed by the integration of a terminology server and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system is evaluated for its practical use by transforming LDTv.2 laboratory results into MIO lab reports according to NASHIP specifications and into to the HL7 Europe Laboratory Report. It is shown that the system can generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. CONCLUSIONS The tool for the auto-generation of Mirth channels was successfully presented. Initial tests have shown that it is feasible to use the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and FHIR are well established in the field of medical informatics, the combination offers space for more research, especially with regard to the FHIR Mapping Language. At the same time, it can be stated that the Mapping Language still has implementation short comings that can be compensated by Mirth Connect as a base technology. CLINICALTRIAL N/A

Purpose Childhood cancer survivors (CCS) are at risk for increased morbidity and reduced quality of life associated with treatment-related late effects. In Germany, however, only a few of the more than 40,000 CCS registered in the German Childhood Cancer Registry (GCCR) currently benefit from adequate clinical long-term follow-up (LTFU) structures. To establish a comprehensive knowledge base on CCS’ long-term health in Germany, a database was developed in cooperation with the GCCR. Following a first evaluation phase at two German university centres, this database will be implemented more widely within Germany allowing longitudinal documentation of clinical LTFU data. Methods The feasibility study cohort comprised 208 CCS aged 18 or older whose medical, mental and psychosocial health data were collected during routine LTFU or first clinic visits in adult care. CCS were enrolled from 04/2021 to 12/2022, and data entry was completed by 03/2023. Descriptive data analysis was conducted. All CCS were stratified into three risk groups (RG) based on their individual risk for developing late effects resulting from their respective diagnoses and treatments. Results Chronic health conditions of various organ systems associated with late and long-term effects of cancer therapy affected CCS in all RG supporting the clinical relevance of risk-adapted LTFU. Enrolment into the database was feasible and broadly accepted amongst CCS. Conclusion Implementation of a clinical follow-up care infrastructure and database in Germany will pave the way to collect clinically evaluated and regularly updated health data of potentially over 40,000 German CCS and facilitate future national and international cooperation.

Background: Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term “metadata” and its use is not always unambiguous. Objective: This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse. Methods: A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used. Results: The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas. Conclusions: Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context.

The primary intention of any scientific work is to share the gained knowledge and to contribute to the knowledge and progress in the scientific domain. The wide range of journals and conferences, each with specific submission requirements, can be difficult to navigate, especially for young scientists without extensive experience. But a suitable publication strategy can be helpful, especially at the beginning of a scientific career. Using the annual conference of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS) e.V. as an example, this editorial highlights fundamental differences, advantages and disadvantages, as well as assistance in selecting the right form of submission.