BACKGROUND: Evidence-based medicine (EBM) has the potential to improve health outcomes, but EBM has not been widely integrated into the systems used for research or clinical decision-making. There has not been a scalable and reusable computer-readable standard for distributing research results and synthesized evidence among creators, implementers, and the ultimate users of that evidence. Evidence that is more rapidly updated, synthesized, disseminated, and implemented would improve both the delivery of EBM and evidence-based health care policy.
OBJECTIVE: This study aimed to introduce the EBM on Fast Healthcare Interoperability Resources (FHIR) project (EBMonFHIR), which is extending the methods and infrastructure of Health Level Seven (HL7) FHIR to provide an interoperability standard for the electronic exchange of health-related scientific knowledge.
METHODS: As an ongoing process, the project creates and refines FHIR resources to represent evidence from clinical studies and syntheses of those studies and develops tools to assist with the creation and visualization of FHIR resources.
RESULTS: The EBMonFHIR project created FHIR resources (ie, ArtifactAssessment, Citation, Evidence, EvidenceReport, and EvidenceVariable) for representing evidence. The COVID-19 Knowledge Accelerator (COKA) project, now Health Evidence Knowledge Accelerator (HEvKA), took this work further and created FHIR resources that express EvidenceReport, Citation, and ArtifactAssessment concepts. The group is (1) continually refining FHIR resources to support the representation of EBM; (2) developing controlled terminology related to EBM (ie, study design, statistic type, statistical model, and risk of bias); and (3) developing tools to facilitate the visualization and data entry of EBM information into FHIR resources, including human-readable interfaces and JSON viewers.
CONCLUSIONS: EBMonFHIR resources in conjunction with other FHIR resources can support relaying EBM components in a manner that is interoperable and consumable by downstream tools and health information technology systems to support the users of evidence.

BACKGROUND: Precision public health (PPH) can maximize impact by targeting surveillance and interventions by temporal, spatial, and epidemiological characteristics. Although rapid diagnostic tests (RDTs) have enabled ubiquitous point-of-care testing in low-resource settings, their impact has been less than anticipated, owing in part to lack of features to streamline data capture and analysis.
OBJECTIVE: We aimed to transform the RDT into a tool for PPH by defining information and data axioms and an information utilization index (IUI); identifying design features to maximize the IUI; and producing open guidelines (OGs) for modular RDT features that enable links with digital health tools to create an RDT-OG system.
METHODS: We reviewed published papers and conducted a survey with experts or users of RDTs in the sectors of technology, manufacturing, and deployment to define features and axioms for information utilization. We developed an IUI, ranging from 0% to 100%, and calculated this index for 33 World Health Organization-prequalified RDTs. RDT-OG specifications were developed to maximize the IUI; the feasibility and specifications were assessed through developing malaria and COVID-19 RDTs based on OGs for use in Kenya and Indonesia.
RESULTS: The survey respondents (n=33) included 16 researchers, 7 technologists, 3 manufacturers, 2 doctors or nurses, and 5 other users. They were most concerned about the proper use of RDTs (30/33, 91%), their interpretation (28/33, 85%), and reliability (26/33, 79%), and were confident that smartphone-based RDT readers could address some reliability concerns (28/33, 85%), and that readers were more important for complex or multiplex RDTs (33/33, 100%). The IUI of prequalified RDTs ranged from 13% to 75% (median 33%). In contrast, the IUI for an RDT-OG prototype was 91%. The RDT open guideline system that was developed was shown to be feasible by (1) creating a reference RDT-OG prototype; (2) implementing its features and capabilities on a smartphone RDT reader, cloud information system, and Fast Healthcare Interoperability Resources; and (3) analyzing the potential public health impact of RDT-OG integration with laboratory, surveillance, and vital statistics systems.
CONCLUSIONS: Policy makers and manufacturers can define, adopt, and synergize with RDT-OGs and digital health initiatives. The RDT-OG approach could enable real-time diagnostic and epidemiological monitoring with adaptive interventions to facilitate control or elimination of current and emerging diseases through PPH.

It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets.
This study\'s objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials.
We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs.
Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date.
The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element\'s (variable\'s) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by \"wrapping\" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium\'s Clinical Data Acquisition Standards Harmonization Model.

HL7 FHIR was created almost a decade ago and is seeing increasingly wide use in high income settings. Although some initial work was carried out in low and middle income (LMIC) settings there has been little impact until recently. The need for reliable and easy to implement interoperability between health information systems in LMICs is growing with large scale deployments of EHRs, national reporting systems and mHealth applications. The OpenMRS open source EHR has been deployed in more than 44 LMIC with increasing needs for interoperability with other HIS. We describe here the development and deployment of a new FHIR module supporting the latest standards and its use in interoperability with laboratory systems, mHealth applications, pharmacy dispensing system and as a tool for supporting advanced user interface designs. We also show how it facilitates date science projects and deployment of machine leaning based CDSS and precision medicine in LMICs.

The care model Hospital@Home offers hospital-level treatment at home, aiming to alleviate hospital strain and enhance patient comfort. Despite its potential, integrating digital health solutions into this care model still remains limited. This paper proposes a concept for integrating laboratory testing at the Point of Care (POC) into Hospital@Home models to improve efficiency and interoperability.
METHODS: Using the HL7 FHIR standard and cloud infrastructure, we developed a concept for direct transmission of laboratory data collected at POC. Requirements were derived from literature and discussions with a POC testing device producer. An architecture for data exchange was developed based on these requirements.
RESULTS: Our concept enables access to laboratory data collected at POC, facilitating efficient data transfer and enhancing interoperability. A hypothetical scenario demonstrates the concept\'s feasibility and benefits, showcasing improved patient care and streamlined processes in Hospital@Home settings.
CONCLUSIONS: Integration of POC data into Hospital@Home models using the HL7 FHIR standard and cloud infrastructure offers potential to enhance patient care and streamline processes. Addressing challenges such as data security and privacy is crucial for its successful implementation into practice.

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.
UNASSIGNED: Die Arbeitsgruppe Interoperabilität der Medizininformatik-Initiative (MII) ist die Plattform für die Abstimmung übergreifender Vorgehensweisen, Datenstrukturen und Schnittstellen zwischen den Datenintegrationszentren (DIZ) der Universitätskliniken und nationalen bzw. internationalen Interoperabilitätsgremien. Ziel ist die gemeinsame inhaltliche und technische Ausgestaltung einer über das Forschungsdatenportal für Gesundheit nutzbaren verteilten Infrastruktur zur Sekundärnutzung klinischer Versorgungsdaten. Wichtige Rahmenbedingungen sind dabei Datenschutz und IT-Sicherheit für die Nutzung von Gesundheitsdaten in der biomedizinischen Forschung. Hierfür werden in dezidierten Taskforces geeignete Methoden eingesetzt, um prozessuale, syntaktische und semantische Interoperabilität für Datennutzungsprojekte zu ermöglichen. So wurde der MII-Kerndatensatz, bestehend aus mehreren Modulen mit zugehörigen Informationsmodellen, entwickelt und mittels des Standards HL7® FHIR® implementiert, um fachliche und technische Vorgaben für die interoperable Datenbereitstellung von Versorgungsdaten durch die DIZ zu ermöglichen. Zur näheren Beschreibung dieser Datensätze dienen internationale Terminologien und konsentierte Metadaten. Die Gesamtarchitektur, einschließlich übergreifender Schnittstellen, setzt die methodischen und rechtlichen Anforderungen an eine verteilte Datennutzungsinfrastruktur z. B. durch Bereitstellung pseudonymisierter Daten oder föderierte Analysen um. Mit diesen Ergebnissen der Arbeitsgruppe Interoperabilität stellt die MII eine zukunftsweisende Lösung für den Austausch und die Nutzung von Routinedaten vor, deren Anwendbarkeit über den Zweck der Forschung hinausgeht und eine wesentliche Rolle in der digitalen Transformation des Gesundheitswesens spielen kann.

BACKGROUND: The Fast Health Interoperability Resources (FHIR) standard was proposed and released to solve the interoperability problems of the electronic health records. The FHIR Subscription resources are used to establish real-time event notifications from the FHIR server to another system. There are several communication channels such as rest-hook and websocket. The objective of our work is to compare the performance of the FHIR subscription using the rest-hook and websocket channels.
METHODS: HAPI FHIR server, python websocket clients and HTTP endpoints were used to measure the processor and memory usage of the two subscription channels. Tests were performed with 5, 10, 15, 20, 30, 40, 50, 60, 70 and 80 clients. The performance was logged using windows performance monitor.
RESULTS: The rest-hook subscription showed near six-fold increase in resource utilization when increasing the clients from 5 to 80. On the contrary, the websocket subscription channel did not reach a two-fold increase.
CONCLUSIONS: The type of the subscription channel should be carefully selected and load distribution should be considered when the number of clients grows.

BACKGROUND: Electronic health records (EHR) emerged as a digital record of the data that is generated in the healthcare.
OBJECTIVE: In this paper the transfer times of EHRs using the Hypertext Transfer Protocol and WebSocket in both local network and wide area network (WAN) are compared.
METHODS: A python web application to serve Fast Health Interoperability Resources (FHIR) records is created and the transfer times of the EHRs over both HTTP and WebSocket connection are measured. 45000 test Patient resources in 20, 50, 100 and 200 resources per Bundle transfers are used.
RESULTS: WebSocket showed much better transfer times of large amount of data. These were 18 s shorter in the local network and 342 s shorter in WAN for the 20 resource per Bundle transfer.
CONCLUSIONS: RESTful APIs are a convenient way to implement EHR servers; on the other hand, HTTP becomes a bottleneck when transferring large amount of data. WebSocket shows better transfer times and thus its superiority in such situations. The problem can be addressed by developing a new communication protocol or by using network tunneling to handle large data transfer of EHRs.

BACKGROUND: The Fast Healthcare Interoperability Resources (FHIR) and Clinical Document Architecture (CDA) are standards for the healthcare industry, designed to improve the exchange of health data by interoperability. Both standards are constrained through what are known as Implementation Guides (IG) for specific use.
OBJECTIVE: Both of these two standards are widely in use and play an important role in the Austrian healthcare system. Concepts existing in CDA and FHIR must be aligned between both standards.
METHODS: Many existing approaches are presented and discussed, none are fully suited to the needs in Austria.
RESULTS: The IG Publisher has already been used for CDA IGs, beside of its intended FHIR support, but never for both in one IG. Even the International Patient Summary (IPS), existing as CDA and FHIR specification, does not solve the needed comparability between these two.
CONCLUSIONS: As the IG Publisher is widely used and supports CDA, it should be used for Dual Implementation Guides. Further work and extension of IG Publisher is necessary to enhance the readability of the resulting IGs.

UNASSIGNED: Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans.
UNASSIGNED: We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists.
UNASSIGNED: Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise.
UNASSIGNED: An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.