Journal:Using knowledge graph structures for semantic interoperability in electronic health records data exchanges

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Full article title Using knowledge graph structures for semantic interoperability in electronic health records data exchanges
Journal Information
Author(s) Sachdeva, Shelly; Bhalla, Subhash
Author affiliation(s) National Institute of Technology Delhi, University of Aizu
Primary contact Email: shellysachdeva at nitdelhi dot ac dot in
Year published 2022
Volume and issue 13(2)
Article # 52
DOI 10.3390/info13020052
ISSN 2078-2489
Distribution license Creative Commons Attribution 4.0 International
Website https://www.mdpi.com/2078-2489/13/2/52/htm
Download https://www.mdpi.com/2078-2489/13/2/52/pdf (PDF)

Abstract

Information sharing across medical institutions is restricted to information exchange between specific partners. The lifelong electronic health record (EHR) structure and content require standardization efforts. Existing standards such as openEHR, Health Level 7 (HL7), and CEN TC251 EN 13606 (Technical committee on Health Informatics of the European Committee for Standardization) aim to achieve data independence along with semantic interoperability. This study aims to discover knowledge representation to achieve semantic health data exchange. openEHR and CEN TC251 EN 13606 use archetype-based technology for semantic interoperability. The HL7 Clinical Document Architecture is on its way to adopting this through HL7 templates. Archetypes are the basis for knowledge-based systems, as these are means to define clinical knowledge.

The paper examines a set of formalisms for the suitability of describing, representing, and reasoning about archetypes. Each of the information exchange technologies—such as XML, Web Ontology Language (OWL), Object Constraint Language (OCL), and Knowledge Interchange Format (KIF)—is evaluated as a part of the knowledge representation experiment. These examine the representation of archetypes as described by Archetype Definition Language (ADL). The evaluation maintains a clear focus on the syntactic and semantic transformations among different EHR standards.

Keywords: archetypes, electronic health records, dual-model approach, knowledge representation, EHR, XML, ADL, OWL, KIF

Introduction

Healthcare is a continuously evolving domain. New findings of diseases and clinical treatments are continuously being made. It has raised the need for increased information exchange among various medical institutions. Electronic health records (EHRs) contain the medical history and treatments of the patients at those medical institutions. In the classical approach, information and knowledge are stored together. However, storage of each clinical concept in a single relation led to a huge data model that was difficult to manage and expensive to maintain. Among the existing interoperability approaches for EHRs, the dual-model approach [1] seems to be most promising. It consists of an information layer and a knowledge layer. The key benefit of this approach is the segregation of knowledge (represented as archetypes [2]). A conceptual idea is virtually transferred through the medium of an intermediate structure of a knowledge graph. This study analyzes that knowledge graph's components.

Knowledge graphs are used to capture knowledge in application-based situations that require large-scale integration, management, and extraction of value from a variety of data sources. Recent studies examine all currently available knowledge graphs (KGs), including their characteristics, approaches, applications, issues, and challenges. [3,4]

This paper focuses on using knowledge representation and information interchange technologies for archetype representation.

Overview of EHRs

The domain of the modern EHR is complex. It consists of different types of data (from textual to multimedia), with new data requirements emerging over time. For example, there are about 300,000 medical terms at present (as defined by SNOMED CT), and medical tests and procedures are constantly created and modified. EHRs have a complex structure based on archetypes. These may include data based on hundreds of parameters, such as temperature, blood pressure, and body mass index (BMI). Each of the individual parameters (or concepts) has its own specific content and is represented as an archetype. For example, one archetype could contain an item such as "data," which can, for example, be represented as a documented heart rate observation. This archetype ideally offers complete knowledge about a clinical context (i.e., attributes of data), the data's "state" (i.e., context for interpretation of data), and its "protocol" (i.e., information regarding the gathering of data) (see Appendix A). Various standards development organizations are working to improve the interoperability of semantic EHRs through these archetypes and more.

It is desirable to have EHR systems that are functionally and semantically interoperable systems. Interoperability can be defined as an ability to communicate data such that the data are sufficient to perform the tasks at the receiving system. The associated data items have the same meaning for the creator of the sending party and the users of the receiving party, and the tasks performed using the data must be to the satisfaction of the receiving party. To tackle the EHR interoperability problem, many authorized organizations have defined several standards. Examples include Health Level 7 (HL7) and its Clinical Document Architecture (CDA), ASTM International's Continuity of Care Record (CCR), European Committee for Standardization (CEN) Technical Committee 251 and International Organization for Standardization (ISO)'s ISO/EN 13606, and the openEHR Foundation's openEHR. The main objective of all these EHR standards is to structure the data and mark up the content of the medical information to be more readily exchanged.

For this work, three levels of interoperability stand out, namely syntactic (data) interoperability, structural interoperability/semantic interpretability, and semantic interoperability. [5] The main mechanisms for interoperability are reference models, archetypes, and domain knowledge governance.

Archetypes and semantic interoperability

References

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added. The original references are in alphabetical order; this version places them in or order of appearance, by design.