Cross border semantic interoperability for learning health systems: The EHR4CR semantic resources and services

Daniel, Christel; Ouagne, David; Sadou, Éric; Paris, Nicolas; Hussain, Sajjad; Jaulent, Marie-Christine

doi:10.1002/lrh2.10014

Cited by 16 publications

(21 citation statements)

References 39 publications

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“…Interest in LHSs has spread across the globe [35,36,37,38]. Specifically, in the European Community, the TRANSFoRm project has addressed some of the challenges of achieving a robust infrastructure for LHSs [39,40].…”

Section: Learning Health Systems and Learning Cyclesmentioning

confidence: 99%

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Toward an Information Infrastructure for Global Health Improvement

Friedman¹,

Rubin²,

Sullivan³

2017

Yearb Med Inform

110

108

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SummaryProfound global challenges to individual and population health, alongside the opportunities to benefit from digital technology, have spawned the concept of the Learning Health System. Learning Health Systems (LHSs)--which can function at organizational, network, regional, and national levels of scale--have the capability of continuous data-driven self-study that promotes change and improvement. The LHS concept, which originated in the U.S. in 2007, is rapidly gaining attention around the world. LHSs require, but also transcend, the secondary use of health data. This paper describes the key features of LHSs, argues that effective and sustainable LHSs must be supported by infrastructures that allow them to function with economies of scale and scope, and describes the services that such infrastructures must provide. While it is relatively straightforward to describe LHSs, achieving them at the high level of capability necessary to promote significant health benefits will require advancements in science and engineering, engaging the field of informatics among a wider range of disciplines. It also follows from this vision that LHSs cannot be built from an imposed blueprint; LHSs will more likely evolve from efforts at smaller scales that compose into larger systems.

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Section: Learning Health Systems and Learning Cyclesmentioning

confidence: 99%

“…In the U.S., individual organizations such as Intermountain Healthcare [81] Johns Hopkins [60], and the Mayo Clinic [82] [85]. European initiatives such as TRANSFoRm [39] and EHR4CR [38] complement this list.…”

Section: Toward a Health Improvement Ecosystemmentioning

confidence: 99%

Toward an Information Infrastructure for Global Health Improvement

Friedman¹,

Rubin²,

Sullivan³

2017

Yearb Med Inform

110

108

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“…The second trend consists in bridging the EHR with external analytical tools through a complex ETL process that involves both data normalization and semantic alignment. Most systems available today, either in research & developement such as EHR4CR [11,196], DebugIT [197], and i2b2, or as commercial products are based on such types of bridges. An important challenge is about the nature of data.…”

Section: H Clinical Practice and Research Integrationmentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

158

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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“…Technical Reports are similar to research reports but describe models or architectures underlying any aspect of an LHS. This issue features a report by Christel Daniel and colleagues addressing the technical methods required to achieve cross‐border interoperability …”

Section: The Journalmentioning

confidence: 99%

The science of Learning Health Systems: Foundations for a new journal

Friedman

Allee

Delaney

et al. 2016

Learning Health Systems

131

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| THE IMPERATIVESIn recent years, health systems across the United States and around the world have faced persistent challenges including the underutilization of necessary care, the overutilization of inappropriate care, rising costs, disparities in access to care, patient safety concerns, System-level improvement requires a broad and diverse intellectual community. The necessary transdisciplinary community will be composed of scholars with expertise in social, political, technical, and clinical fields, along with many others who bring critical experience from practice. It follows that a third imperative is to form this community and provide it with a gathering place for its evolving scientific ideas and insights.

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Cross border semantic interoperability for learning health systems: The EHR4CR semantic resources and services

Cited by 16 publications

References 39 publications

Toward an Information Infrastructure for Global Health Improvement

Toward an Information Infrastructure for Global Health Improvement

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

The science of Learning Health Systems: Foundations for a new journal

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