An Interactive Visualization Tool for HL7 FHIR Specification Browsing and Profiling

Hong, Na; Wang, Kui; Wu, Sizhu; Shen, Feichen; Yao, Lixia; Jiang, Guoqian

doi:10.1007/s41666-018-0043-8

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…From a platform point of view, we considered the implementation of mobile/tablet apps [ 3 , 9 , 28 , 35 , 39 , 51 , 57 , 60 , 65 , 70 , 72 , 77 , 82 ], standalone apps/servers [ 3 , 23 , 51 , 58 , 61 ], web services/API [ 3 , 11 , 14 , 26 , 34 , 36 , 40 , 48 , 53 , 56 , 71 , 78 ], and web-based tools/applications [ 18 , 26 , 37 , 40 , 42 , 46 , 55 , 69 , 78 ] categories. From a conceptual framework, we considered the categories of general FHIR implementation [ 48 , 59 ], using SMART on FHIR [ 3 , 14 , 22 , 31 ], HL7 API (HAPI)-FHIR server/library/applications [ 14 , 16 , 20 , 32 , 34 - 36 , 40 , 43 , 51 , 54 , 55 , 73 , 76 , 81 , 82 ], and FHIR general framework [ 14 , 40 , 78 ]. In con...…”

Section: Resultsmentioning

confidence: 99%

“…From a conceptual framework, we considered the categories of general FHIR implementation [ 48 , 59 ], using SMART on FHIR [ 3 , 14 , 22 , 31 ], HL7 API (HAPI)-FHIR server/library/applications [ 14 , 16 , 20 , 32 , 34 - 36 , 40 , 43 , 51 , 54 , 55 , 73 , 76 , 81 , 82 ], and FHIR general framework [ 14 , 40 , 78 ]. In consideration of compatibility, we chose FHIR data model/data exchange [ 5 , 11 , 26 , 45 , 55 , 66 , 67 , 73 ] and defining ontology to align with FHIR [ 44 , 68 , 69 ] as the main categories. In addition, we classified all implementation-related work under the miscellaneous category [ 21 , 25 , 30 , 32 , 47 , 60 , 71 ], such as FHIR implementation of the legacy clinical data repository system, FHIR implementation of the agent-based system, implementation of operational data model metadata [ 86 ] to FHIR questionnaire resource implementation, FHIR implementation of the electronic treatment form, Clinical Asset Mapping Program for FHIR, integration of the architecture of domain information model (ISO/IEEE 11073 DIM) [ 31 ] with FHIR, and FHIR-based decision support systems.…”

Section: Resultsmentioning

confidence: 99%

“…To address this question, we reviewed the literature in the FHIR domain and investigated various techniques, methods, and mechanisms used in the implementation of FHIR in the health care sector. [48,59], using SMART on FHIR [3,14,22,31], HL7 API (HAPI)-FHIR server/library/applications [14,16,20,32,[34][35][36]40,43,51,54,55,73,76,81,82], and FHIR general framework [14,40,78]. In consideration of compatibility, we chose FHIR data model/data exchange [5,11,26,45,55,66,67,73] and defining ontology to align with FHIR [44,68,69] as the main categories.…”

Section: Sq1: What Are the Types Or Models Of Fhir Implementation?mentioning

confidence: 99%

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The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities

Ayaz¹,

Pasha²,

Alzahrani³

et al. 2021

JMIR Med Inform

127

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Background Information technology has shifted paper-based documentation in the health care sector into a digital form, in which patient information is transferred electronically from one place to another. However, there remain challenges and issues to resolve in this domain owing to the lack of proper standards, the growth of new technologies (mobile devices, tablets, ubiquitous computing), and health care providers who are reluctant to share patient information. Therefore, a solid systematic literature review was performed to understand the use of this new technology in the health care sector. To the best of our knowledge, there is a lack of comprehensive systematic literature reviews that focus on Fast Health Interoperability Resources (FHIR)-based electronic health records (EHRs). In addition, FHIR is the latest standard, which is in an infancy stage of development. Therefore, this is a hot research topic with great potential for further research in this domain. Objective The main aim of this study was to explore and perform a systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applications. Methods In January 2020, we searched articles published from January 2012 to December 2019 via all major digital databases in the field of computer science and health care, including ACM, IEEE Explorer, Springer, Google Scholar, PubMed, and ScienceDirect. We identified 8181 scientific articles published in this field, 80 of which met our inclusion criteria for further consideration. Results The selected 80 scientific articles were reviewed systematically, and we identified open questions, challenges, implementation models, used resources, beneficiary applications, data migration approaches, and goals of FHIR. Conclusions The literature analysis performed in this systematic review highlights the important role of FHIR in the health care domain in the near future.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Sq1: What Are the Types Or Models Of Fhir Implementation?mentioning

confidence: 99%

See 1 more Smart Citation

The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities

Ayaz¹,

Pasha²,

Alzahrani³

et al. 2021

JMIR Med Inform

127

View full text Add to dashboard Cite

show abstract

“…Health data are generated by all institutes by using open-source web data, but no such online module has been proposed for integration of all web-based centralized. In their study, Hong, Wang, et al [47] revealed a Web-based FHIR visualization tool, using a standard structured format API. Again, Lopes, Bastião, and Oliveira [48] described that the file sharing between users was difficult for heterogeneous data.…”

Section: How Does the Data Harmonization Resolve The Issues Of Heterogeneity?mentioning

confidence: 99%

“…Domain Contributions [43] Oil and Gas High performance measure [44] Agriculture High performance, high availability, and high scalability, using the latest techniques [45] General-Purpose Data generation, storing, fetching, analysis, visualization, and decision-making [46] Banking Helps in auditing the multisource data [47] Healthcare Facilitate for navigation of HL7 FHIR core resources [48] General-Purpose Delivering automatic services to interoperable system [49] Healthcare Helps in developing an automatic system for disordered patient [50] Education To motivate researchers and academicians about the latest techniques [10] Healthcare Useful for decisions of scientific, clinical, and administrative work [51] Education Facilitate in online learning, storage, processing, and academic activities [52] General-Purpose Recommendation system, opinion mining, and parallelism can be targeted [53] Oil and Gas Helpful for decision-makers during exploration, drilling, and production [54] General-Purpose It will facilitate for fetching data and performance measure [65] Healthcare Helpful for disease prevention, tracking, and policy making [11] Healthcare Helps in boosting statistical power of sustainable and robust data [55] Infrastructure Geographic based smart city for aggregation, visualization, and analysis [56] Healthcare Helps in predicting the clinical codes of patient stays…”

Section: Study Referencementioning

confidence: 99%

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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As data size increases drastically, its variety also increases. Investigating such heterogeneous data is one of the most challenging tasks in information management and data analytics. The heterogeneity and decentralization of data sources affect data visualization and prediction, thereby influencing analytical results accordingly. Data harmonization (DH) corresponds to a field that unifies the representation of such a disparate nature of data. Over the years, multiple solutions have been developed to minimize the heterogeneity aspects and disparity in formats of big-data types. In this study, a systematic review of the literature was conducted to assess the state-of-the-art DH techniques. This study aimed to understand the issues faced due to heterogeneity, the need for DH and the techniques that deal with substantial heterogeneous textual datasets. The process produced 1355 articles, but among them, only 70 articles were found to be relevant through inclusion and exclusion criteria methods. The result shows that the heterogeneity of structured, semi-structured, and unstructured (SSU) data can be managed by using DH and its core techniques, such as text preprocessing, Natural Language Preprocessing (NLP), machine learning (ML), and deep learning (DL). These techniques are applied to many real-world applications centered on the information-retrieval domain. Several assessment criteria were implemented to measure the efficiency of these techniques, such as precision, recall, F-1, accuracy, and time. A detailed explanation of each research question, common techniques, and performance measures is also discussed. Lastly, we present readers with a detailed discussion of the existing work, contributions, and managerial and academic implications, along with the conclusion, limitations, and future research directions.

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