PCEtoFHIR: Decomposition of Postcoordinated SNOMED CT Expressions for Storage as HL7 FHIR Resources.

BACKGROUND: To ensure interoperability, both structural and semantic standards must be followed. For exchanging medical data between information systems, the structural standard FHIR (Fast Healthcare Interoperability Resources) has recently gained popularity. Regarding semantic interoperability, the reference terminology SNOMED Clinical Terms (SNOMED CT), as a semantic standard, allows for postcoordination, offering advantages over many other vocabularies. These postcoordinated expressions (PCEs) make SNOMED CT an expressive and flexible interlingua, allowing for precise coding of medical facts. However, this comes at the cost of increased complexity, as well as challenges in storage and processing. Additionally, the boundary between semantic (terminology) and structural (information model) standards becomes blurred, leading to what is known as the TermInfo problem. Although often viewed critically, the TermInfo overlap can also be explored for its potential benefits, such as enabling flexible transformation of parts of PCEs. OBJECTIVE: In this paper, an alternative solution for storing PCEs is presented, which involves combining them with the FHIR data model. Ultimately, all components of a PCE should be expressible solely through precoordinated concepts that are linked to the appropriate elements of the information model. METHODS: The approach involves storing PCEs decomposed into their components in alignment with FHIR resources. By utilizing the Web Ontology Language (OWL) to generate an OWL ClassExpression, and combining it with an external reasoner and semantic similarity measures, a precoordinated SNOMED CT concept that most accurately describes the PCE is identified as a Superconcept. In addition, the nonmatching attribute relationships between the Superconcept and the PCE are identified as the "Delta." Once SNOMED CT attributes are manually mapped to FHIR elements, FHIRPath expressions can be defined for both the Superconcept and the Delta, allowing the identified precoordinated codes to be stored within FHIR resources. RESULTS: A web application called PCEtoFHIR was developed to implement this approach. In a validation process with 600 randomly selected precoordinated concepts, the formal correctness of the generated OWL ClassExpressions was verified. Additionally, 33 PCEs were used for two separate validation tests. Based on these validations, it was demonstrated that a previously proposed semantic similarity calculation is suitable for determining the Superconcept. Additionally, the 33 PCEs were used to confirm the correct functioning of the entire approach. Furthermore, the FHIR StructureMaps were reviewed and deemed meaningful by FHIR experts. CONCLUSIONS: PCEtoFHIR offers services to decompose PCEs for storage within FHIR resources. When creating structure mappings for specific subdomains of SNOMED CT concepts (eg, allergies) to desired FHIR profiles, the use of SNOMED CT Expression Templates has proven highly effective. Domain experts can create templates with appropriate mappings, which can then be easily reused in a constrained manner by end users.

Desiderata for a Synthetic Clinical Data Generator.

The current movement in Medical Informatics towards comprehensive Electronic Health Records (EHRs) has enabled a wide range of secondary use cases for this data. However, due to a number of well-justified concerns and barriers, especially with regards to information privacy, access to real medical records by researchers is often not possible, and indeed not always required. An appealing alternative to the use of real patient data is the employment of a generator for realistic, yet synthetic, EHRs. However, we have identified a number of shortcomings in prior works, especially with regards to the adaptability of the projects to the requirements of the German healthcare system. Based on three case studies, we define a non-exhaustive list of requirements for an ideal generator project that can be used in a wide range of localities and settings, to address and enable future work in this regard.

Motivating Developers to Use Interoperable Standards for Data in Pandemic Health Apps.

The COVID-19 pandemic has brought along a massive increase in app development. However, most of these apps are not using interoperable data. The COMPASS project of the German COVID-19 Research Network of University Medicine ("Netzwerk Universitätsmedizin (NUM)") tackles this issue, by offering open-source technology, best practice catalogues, and suggestions for designing interoperable pandemic health applications (https://www.netzwerk-universitaetsmedizin.de/projekte/compass). Therefore, COMPASS conceived a framework that includes automated conformity checks as well as reference implementations for more efficient and pandemic-tailored app developments. It further aims to motivate and support developers to use interoperable standards.

Fast Healthcare Interoperability Resources (FHIR®) Representation of Medication Data Derived from German Procedure Classification Codes (OPS) Using Identification of Medicinal Products (IDMP) Compliant Terminology.

Electronic documentation of medication data is one of the biggest challenges associated with digital clinical documentation. Despite its importance, it has not been consistently implemented in German university hospitals. In this paper we describe the approach of the German Medical Informatics Initiative (MII) towards the modelling of a medication core dataset using FHIR® profiles and standard-compliant terminologies. The FHIR profiles for Medication and MedicationStatement were adapted to the core dataset of the MIl. The terminologies to be used were selected based on the criteria of the ISO-standard for the Identification of Medicinal Products (IDMP). For a first use case with a minimal medication dataset, the entries in the medication chapter of the German Procedure Classification (OPS codes) were analyzed and mapped to IDMP-compliant medication terminology. OPS data are available at all German hospitals as they are mandatory for reimbursement purposes. Reimbursement-relevant encounter data containing OPS medication procedures were used to create a FHIR representation based on the FHIR profiles MedicationStatement and Medication. This minimal solution includes - besides the details on patient and start-/end-dates - the active ingredients identified by the IDMP-compliant codes and - if specified in the OPS code - the route of administration and the range of the amount of substance administered to the patient, using the appropriate unit of measurement code. With FHIR, the medication data can be represented in the data integration centers of the MII to provide a standardized format for data analysis across the MII sites.

The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond.

BACKGROUND: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine. METHODS: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. RESULTS: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. CONCLUSION: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Towards Interoperability in Clinical Research - Enabling FHIR on the Open-Source Research Platform XNAT.

This paper presents an approach to enable interoperability of the research data management system XNAT by the implementation of the HL7 standards framework Fast Healthcare Interoperability Resources (FHIR). The FHIR implementation is realized as an XNAT plugin (Source code: https://github.com/somnonetz/xnat-fhir-plugin ), that allows easy adoption in arbitrary XNAT instances. The approach is demonstrated on patient data exchange between a FHIR reference implementation and XNAT.

Standardizing Germany's Electronic Disease Management Program for Bronchial Asthma.

Disease management programs coordinate and manage treatment between physicians and across sectors of the healthcare system. The aim is to reduce existing care deficits (overuse, underuse and misuse) and thus improve the quality and cost-effectiveness of care. To facilitate the treatment of chronic diseases such as asthma, it is important to continuously document a patient's medical history. For this purpose, it is necessary to be able to integrate and exchange data from and between multiple different information systems. Aiming to ensure interoperability across electronic documentation systems, this paper proposes the standardization of the KBV's (National Association of Statutory Health Insurance in Germany) specification for the electronic Disease Management Program (eDMP) for bronchial asthma. Therefore, international standards like SNOMED CT, LOINC and UCUM were chosen to encode clinical information, while evaluating their suitability with the scoring system ISO/PRF TR 21564. The resulting analysis showed that most of the terms had either a complete or partial equivalent term in one of the terminology systems. Therefore, future implementations of the eDMP for bronchial asthma that utilize standard terminologies could benefit from data integration from different sources like electronic health records and reduce redundancies in data capture and storage.

Why digital medicine depends on interoperability.

Digital data are anticipated to transform medicine. However, most of today's medical data lack interoperability: hidden in isolated databases, incompatible systems and proprietary software, the data are difficult to exchange, analyze, and interpret. This slows down medical progress, as technologies that rely on these data - artificial intelligence, big data or mobile applications - cannot be used to their full potential. In this article, we argue that interoperability is a prerequisite for the digital innovations envisioned for future medicine. We focus on four areas where interoperable data and IT systems are particularly important: (1) artificial intelligence and big data; (2) medical communication; (3) research; and (4) international cooperation. We discuss how interoperability can facilitate digital transformation in these areas to improve the health and well-being of patients worldwide.