Data Sharing in Distributed Architectures – Concept and Implementation in HiGHmed

Wettstein, Reto; Hund, Hauke; Fegeler, Christian; Heinze, Oliver

doi:10.3233/shti210548

Cited by 7 publications

(8 citation statements)

References 8 publications

(12 reference statements)

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“…Of the 49 studies, most (73%, n=36) studies covered the research domain of clinical research, of which 10 (20%) studies were clinical trials [ 22 , 29 - 31 , 36 , 39 , 43 , 56 , 65 , 66 ]; 3 (6%) studies focused on solutions in public health and epidemiology [ 38 , 40 , 64 ], and the remaining studies did not specify their research domain (20%, n=10; Figure 4 ) [ 24 , 32 , 41 , 42 , 45 - 47 , 50 , 63 , 69 ]. The included studies used FHIR for the standardization of data (41%, n=20) [ 23 , 26 , 30 , 34 , 41 , 45 - 48 , 51 - 53 , 57 - 60 , 63 , 66 , 67 , 70 ], data capture (29%, n=14) [ 1 , 12 , 22 , 24 , 27 , 35 - 37 , 43 , 44 , 55 , 61 , 64 , 65 ], recruitment (14%, n=7) [ 28 , 29 , 31 , 32 , 49 , 56 , 62 ], analysis (12%, n=6) [ 25 , 38 , 42 , 50 , 68 , ...…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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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Section: Resultsmentioning

confidence: 99%

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Section: Study Objectivesmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The aforementioned questions are addressed by the pAItient project (Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence-based evaluation of clinical value), which has the objective to establish an AI innovation environment as a proof-of-concept extension of the already existing Medical Data Integration Center [ 18 , 19 ] at the Heidelberg University Hospital [ 20 ]. Project partners include the German Cancer Research Center (DKFZ), the German Research Center for Artificial Intelligence (DFKI), and MINT Medical GmbH (Heidelberg, Germany) [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Stakeholder Requirements to Enable Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Results of a Multistep Mixed Methods Study

Weinert¹,

Klass²,

Schneider³

et al. 2023

JMIR Form Res

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Background Legal, controlled, and regulated access to high-quality data from academic hospitals currently poses a barrier to the development and testing of new artificial intelligence (AI) algorithms. To overcome this barrier, the German Federal Ministry of Health supports the “pAItient” (Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence-based evaluation of clinical value) project, with the goal to establish an AI Innovation Environment at the Heidelberg University Hospital, Germany. It is designed as a proof-of-concept extension to the preexisting Medical Data Integration Center. Objective The first part of the pAItient project aims to explore stakeholders’ requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data. Methods We designed a multistep mixed methods approach. First, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants’ answers and distributed among the stakeholders’ organizations. In addition, patients and physicians were interviewed. Results The identified requirements covered a wide range and were conflicting sometimes. Relevant patient requirements included adequate provision of necessary information for data use, clear medical objective of the research and development activities, trustworthiness of the organization collecting the patient data, and data should not be reidentifiable. Requirements of AI researchers and developers encompassed contact with clinical users, an acceptable user interface (UI) for shared data platforms, stable connection to the planned infrastructure, relevant use cases, and assistance in dealing with data privacy regulations. In a next step, a requirements model was developed, which depicts the identified requirements in different layers. This developed model will be used to communicate stakeholder requirements within the pAItient project consortium. Conclusions The study led to the identification of necessary requirements for the development, testing, and validation of AI applications within a hospital-based generic infrastructure. A requirements model was developed, which will inform the next steps in the development of an AI innovation environment at our institution. Results from our study replicate previous findings from other contexts and will add to the emerging discussion on the use of routine medical data for the development of AI applications. International Registered Report Identifier (IRRID) RR2-10.2196/42208

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“…While these components are not exhaustive, they represent a pattern of technological implementation in modern health care systems that is increasingly common, such as the use of openEHR and IHE XDS together. 16 17 18…”

Section: Introductionmentioning

confidence: 99%

Aligning Semantic Interoperability Frameworks with the FOXS Stack for FAIR Health Data

Meredith¹,

Whitehead

Dacey

2022

Methods Inf Med

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Background: FAIR Guiding Principles present a synergy with the use cases for digital health records, in that clinical data needs to be found, accessible within a range of environments, data must interoperate between systems and subsequently reused. The use of HL7 FHIR, openEHR, IHE XDS and SNOMED CT (FOXS) together represent a specification to create an open digital health platform for modern healthcare applications. Objectives: To describe where logical FOXS components align to the European Open Science Cloud Interoperability Framework (EOSC-IF) reference architecture for semantic interoperability. This should provide a means of defining if FOXS aligns to FAIR principles and establish the data models and structures that support longitudinal care records as being fit to underpin scientific research. Methods: The EOSC-IF Semantic View is a representation of semantic interoperability where meaning is preserved between systems and users. This was analysed and cross referenced with FOXS architectural components, mapping concepts and objects that describe content such as catalogues and semantic artefacts. Results: Majority of conceptual Semantic View components were featured within FOXS architecture. Semantic Business Objects are composed of a range of elements such as openEHR archetypes and templates, FHIR resources and profiles, SNOMED CT concepts and XDS document identifiers. Semantic Functional Content comprises of catalogues of metadata were also supported by openEHR and FHIR tools. Conclusions: Despite some elements of EOSC-IF being vague (e.g., FAIR Digital Object), there was a broad conformance to the framework concepts and the components of a FOXS platform. This work supports a health domain specific view of semantic interoperability, and how this may be achieved to support FAIR data for health research via a standardised framework.

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Data Sharing in Distributed Architectures – Concept and Implementation in HiGHmed

Cited by 7 publications

References 8 publications

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

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

Exploring Stakeholder Requirements to Enable Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Results of a Multistep Mixed Methods Study

Aligning Semantic Interoperability Frameworks with the FOXS Stack for FAIR Health Data

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