Executing Distributed Healthcare and Research Processes – The HiGHmed Data Sharing Framework

Hund, Hauke; Wettstein, Reto; Heidt, Christian M.; Fegeler, Christian

doi:10.3233/shti210060

Cited by 16 publications

(25 citation statements)

References 5 publications

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“…To secure the connection between our central middleware components and local middleware clients, without requiring the university hospitals to open their firewall for outside requests, we chose a pull transport mechanism instead of a push process from the outside. Within CODEX, we evaluated multiple middleware components already developed by various MII partner sites and chose 2 that are already widely used in different consortia and fulfill the requirements mentioned previously: AKTIN broker [ 27 , 44 ] and HiGHmed DSF [ 28 ]. Both were extended to fully comply with the CODEX requirements, leading to a new client release for AKTIN [ 45 ] and the creation of a feasibility process for the DSF [ 46 ], similar to that created by Wettstein et al [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

“…To align the strategy with the other parts of the CODEX project and MII, we evaluated 4 middleware components as part of our project, which had been used previously to transport feasibility queries or used in other parts of the CODEX project to streamline further development. These included the AKTIN broker [ 27 ], data sharing framework (DSF) [ 28 ], connector component federated search [ 29 ], and German Biobank Node Client-Broker [ 30 , 31 ]. We then used the 2 middleware that had the highest scores as a base for further development.…”

Section: Methodsmentioning

confidence: 99%

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The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study

Gruendner¹,

Deppenwiese²,

Köhler³

et al. 2022

JMIR Med Inform

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Background An essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing. Objective This study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data. Methods We analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service. Results We implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative. Conclusions We designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study

Gruendner¹,

Deppenwiese²,

Köhler³

et al. 2022

JMIR Med Inform

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“…Of the 49 studies, the majority were conducted in Germany (47%, n=23) [12,26,[28][29][30][31]34,35,[40][41][42][45][46][47]52,53,[56][57][58]60,62,63,69], the United States (27%, n=13) [22,25,36,44,[48][49][50]61,[64][65][66]68,70], and Australia (6%, n=3) [1,43,67]. The remaining studies were performed in Austria (2%, n=1) [32], Canada (2%, n=1) [24], France (2%, n=1) [51], Greece (2%, n=1) [59], Japan (2%, n=1) [27], Pakistan (2%, n=1) [38], Spain (2%, n=1) [55], Switzerland (2%, n=1) [39], Taiwan (2%, n=1) [23], and the United Kingdom (2%, n=1)…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…In terms of medical specialty, most (55%, 27/49) of the studies [24,[27][28][29][30][31][32]34,[36][37][38][39][40][41][42]44,46,48,49,53,56,[60][61][62][63]67,70] were using a generic approach-implementable in any kind of specialty (Table 2). Of the remaining studies, 16% (8/49) use cases focused on infectious disease [1,22,23,26,45,47,50,59], whereas 12% (6/49) focused on oncology [25,55,57,58,65,66] and 8% (4/49) on genomics [43,52,68,69].…”

Section: Study Objectivesmentioning

confidence: 99%

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

Vorisek¹,

Lehne²,

Klopfenstein³

et al. 2022

JMIR Med Inform

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Background:The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as "Public Health & Research" and "Evidence-Based Medicine" while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. Objective:The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. Methods:We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. Results:We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain "Public Health & Research." Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. Conclusions:Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such...

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“…Based on the open standards Business Process Model and Notation (BPMN 2.0) 2 and Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR R4) 3 , HiGHmed is developing the DSF [9]. It is designed to execute various cross-organizational processes within HiGHmed.…”

Section: State Of the Artmentioning

confidence: 99%

Data Sharing in Distributed Architectures – Concept and Implementation in HiGHmed

Wettstein

Hund

Fegeler

et al. 2021

Studies in Health Technology and Informatics

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Medical routine data has the potential to benefit research. However, transferring this data into a research context is difficult. For this reason Medical Data Integration Centers are being established in German university hospitals to consolidate data from primary information systems in a single location. But, small data-sets from one organization can be insufficient to answer a research question adequately. In order to obtain larger data-sets, attempts to merge and provide data-sets across institutional boundaries are made. Therefore, this paper proposes a possible process that can extract, merge, pseudonymize and provide distributed data-sets from several organizations conforming to privacy regulations. This process is executed according to the open standard BPMN 2.0, the underlying process data model is based on HL7 FHIR R4. The proposed solution is currently being deployed at eight university hospitals and one Trusted Third Party in the HiGHmed consortium.

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Executing Distributed Healthcare and Research Processes – The HiGHmed Data Sharing Framework

Cited by 16 publications

References 5 publications

The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study

The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

Data Sharing in Distributed Architectures – Concept and Implementation in HiGHmed

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