scholarly journals Validation and Testing of Fast Healthcare Interoperability Resources Standards Compliance: Data Analysis (Preprint)

2018 ◽  
Author(s):  
Jason Walonoski ◽  
Robert Scanlon ◽  
Conor Dowling ◽  
Mario Hyland ◽  
Richard Ettema ◽  
...  
Keyword(s):  
Data Analysis ◽  
Health Data ◽  
Technical Solution ◽  
Strongly Correlated ◽  
Data Interoperability ◽  
Testing Tools ◽  
Standards Compliance ◽  
Hl7 Fhir ◽  
Health Care Infrastructure ◽  
Trends Over Time

BACKGROUND There is wide recognition that the lack of health data interoperability has significant impacts. Traditionally, health data standards are complex and test-driven methods played important roles in achieving interoperability. The Health Level Seven International (HL7) standard Fast Healthcare Interoperability Resources (FHIR) may be a technical solution that aligns with policy, but systems need to be validated and tested. OBJECTIVE Our objective is to explore the question of whether or not the regular use of validation and testing tools improves server compliance with the HL7 FHIR specification. METHODS We used two independent validation and testing tools, Crucible and Touchstone, and analyzed the usage and result data to determine their impact on server compliance with the HL7 FHIR specification. RESULTS The use of validation and testing tools such as Crucible and Touchstone are strongly correlated with increased compliance and “practice makes perfect.” Frequent and thorough testing has clear implications for health data interoperability. Additional data analysis reveals trends over time with respect to vendors, use cases, and FHIR versions. CONCLUSIONS Validation and testing tools can aid in the transition to an interoperable health care infrastructure. Developers that use testing and validation tools tend to produce more compliant FHIR implementations. When it comes to health data interoperability, “practice makes perfect.”

scholarly journals Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1747 ◽  
Author(s):  
Cong Peng ◽  
Prashant Goswami
Keyword(s):  
Home Environment ◽  
Open Data ◽  
Health Data ◽  
Self Management ◽  
Formal Ontology ◽  
Data Interoperability ◽  
Semantic Web Technologies ◽  
Ontology Language ◽  
Integration Architecture ◽  
Hl7 Fhir

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

scholarly journals Assessing and piloting interoperability for population health research

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Estupiñán-Romero ◽  
J Gonzalez-García ◽  
E Bernal-Delgado
Keyword(s):  
Case Studies ◽  
Population Health ◽  
Health Research ◽  
Network Architecture ◽  
Digital Health ◽  
Data Extraction ◽  
Health Data ◽  
Research Network ◽  
Technical Solution ◽  
Privacy And Security

Abstract Issue/problem Interoperability is paramount when reusing health data from multiple data sources and becomes vital when the scope is cross-national. We aimed at piloting interoperability solutions building on three case studies relevant to population health research. Interoperability lies on four pillars; so: a) Legal frame (i.e., compliance with the GDPR, privacy- and security-by-design, and ethical standards); b) Organizational structure (e.g., availability and access to digital health data and governance of health information systems); c) Semantic developments (e.g., existence of metadata, availability of standards, data quality issues, coherence between data models and research purposes); and, d) Technical environment (e.g., how well documented are data processes, which are the dependencies linked to software components or alignment to standards). Results We have developed a federated research network architecture with 10 hubs each from a different country. This architecture has implied: a) the design of the data model that address the research questions; b) developing, distributing and deploying scripts for data extraction, transformation and analysis; and, c) retrieving the shared results for comparison or pooled meta-analysis. Lessons The development of a federated architecture for population health research is a technical solution that allows full compliance with interoperability pillars. The deployment of this type of solution where data remain in house under the governance and legal requirements of the data owners, and scripts for data extraction and analysis are shared across hubs, requires the implementation of capacity building measures. Key messages Population health research will benefit from the development of federated architectures that provide solutions to interoperability challenges. Case studies conducted within InfAct are providing valuable lessons to advance the design of a future pan-European research infrastructure.

Omaha System Public Health Data: Analysis Methods

2010 ◽  
Vol 58 (2) ◽  
pp. e22-e23
Author(s):  
Karen A. Monsen ◽  
Karen S. Martin ◽  
Bonnie L Westra
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Health Data ◽  
Public Health Data ◽  
Analysis Methods ◽  
Omaha System ◽  
Data Analysis Methods

scholarly journals Authoring Bioconductor workflows with BiocWorkflowTools

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 431
Author(s):  
Mike L. Smith ◽  
Andrzej K. Oleś ◽  
Wolfgang Huber
Keyword(s):  
Data Analysis ◽  
The Other ◽  
Technical Solution ◽  
Single Source ◽  
Journal Publication ◽  
Source Document ◽  
Manual Intervention ◽  
Starting Point ◽  
The Moment ◽  
High Degree

The Bioconductor Gateway on the F1000Research platform is a channel for peer-reviewed and citable publication of end-to-end data analysis workflows rooted in the Bioconductor ecosystem. In addition to the largely static journal publication, it is hoped that authors will also deposit their workflows as executable documents on Bioconductor, where the benefits of regular code testing and easy updating can be realized. Ideally these two endpoints would be met from a single source document. However, so far this has not been easy, due to lack of a technical solution that meets both the requirements of the F1000Research article submission format and the executable documents on Bioconductor. Submission to the platform requires a LaTeX file, which many authors traditionally have produced by writing an Rnw document for Sweave or knitr. On the other hand, to produce the HTML rendering of the document hosted by Bioconductor, the most straightforward starting point is the R Markdown format. Tools such as pandoc enable conversion between many formats, but typically a high degree of manual intervention used to be required to satisfactorily handle aspects such as floating figures, cross-references, literature references, and author affiliations. The BiocWorkflowTools package aims to solve this problem by enabling authors to work with R Markdown right up until the moment they wish to submit to the platform.

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