Difference between revisions of "Journal:Using knowledge graph structures for semantic interoperability in electronic health records data exchanges"

From LIMSWiki
Jump to navigationJump to search
(Saving and adding more.)
(Saving and adding more.)
Line 47: Line 47:


===Archetypes and semantic interoperability===
===Archetypes and semantic interoperability===
The standards for semantic interoperability (such as CDA, openEHR, and ISO/EN 13606) endorse the two-level modeling approach for storing EHR content. [6] It consists of two layers that propose to segregate information modeling from content (knowledge) modeling. The reference (information) model layer represents the generic structures of components of the healthcare data. The content model on the other hand is used to represent more domain-specific data, which in general have instability due to variability and high rate of change in their usage (e.g., a formal description of a physical examination or prescription).
In openEHR and ISO/EN 13606, the first level is known as the reference model (RM) and the second level consists of archetypes. The RM defines the basic fundamental structure and represents the generic structures of components of the healthcare data at the storage level (i.e., information modeling). At the second layer, the archetype model (AM) constrains the generic structure to encompass logical semantics and, thus, provide a standard definition that aids in semantic interoperability. AM provides deliverables in the form of archetypes and templates. An archetype provides the meta-description of structured clinical records as a computable formalism. In HL7's CDA, the two levels are the Reference Information Model (RIM) and the HL7 templates, which function essentially the same as the archetype concept.
These standards support compatibility among each other. In the case of openEHR and ISO/EN 13606, the only means of achieving interoperability with a generic information model is through their archetypes. In fact, ADL archetypes can be defined against any Unified Modeling Language (UML) model, and it is also possible to write the archetypes against the HL7 Version 3 RIM and the CDA in general. Kilic and Dogac [7] note this in their work, describing how the clinical statements of two different EHR standards derived from the same RIM can be mapped to each other by using archetypes, Refined Message Information Model derivations, and semantic tools.
As mentioned prior, an archetype is an agreed upon formal and interoperable specification of a re-usable clinical data set that underpins an EHR. It captures the maximum possible information about a particular and discrete clinical concept. [2] A conceptual definition of data as archetypes can be developed in terms of constraints on structure, types, values, and behaviors of reference model classes based on the dual-model approach. It consists of the knowledge layer as archetypes and a reference model. An example of a simple archetype is "Weight," which can be used in multiple places as required within an EHR.
Semantics in archetypes have a dual nature. They consist of both structural and terminological components. The structure of an archetype provides support for semantics, while EHR component links form a set of interrelated conceptual, clinical entities. Each entity has a set of terminological bindings associated with it (specified by links to terms of specific medical terminologies).
If data elements are created and modified using archetypes, the archetypes constrain the configuration of data instances to be valid according to the archetype. These are a paradigm for building semantically enabled software systems, providing data validation, clinical modeling (by domain experts), a basis for querying, and form design. An archetype might define or constrain relationships between data values within a data structure. These are expressed as algorithms, formula, or rules. An archetype's [[metadata]] defines its core concept, purpose, use, evidence, authorship, and versioning. An archetype also ensures a maximal dataset. It contains all the relevant information regarding a clinical concept. Once the format of an archetype is agreed upon and published, it is held in a "library’" and made available for use in any part of a given application by multiple vendor systems, multiple institutions, and multiple geographical regions. Each group or entity using the same archetype will understand and compute data captured by the same archetype in another clinical environment. Thus, an archetype serves the following key purposes [8,9]:
# It allows domain experts (clinicians) to capture data for their information systems.
# It provides runtime validation of data input, thus improving data entry quality.
# It provides a basis for intelligent querying of data.
===Representing internal data in archetypes===





Revision as of 22:27, 10 June 2022

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

The standards for semantic interoperability (such as CDA, openEHR, and ISO/EN 13606) endorse the two-level modeling approach for storing EHR content. [6] It consists of two layers that propose to segregate information modeling from content (knowledge) modeling. The reference (information) model layer represents the generic structures of components of the healthcare data. The content model on the other hand is used to represent more domain-specific data, which in general have instability due to variability and high rate of change in their usage (e.g., a formal description of a physical examination or prescription).

In openEHR and ISO/EN 13606, the first level is known as the reference model (RM) and the second level consists of archetypes. The RM defines the basic fundamental structure and represents the generic structures of components of the healthcare data at the storage level (i.e., information modeling). At the second layer, the archetype model (AM) constrains the generic structure to encompass logical semantics and, thus, provide a standard definition that aids in semantic interoperability. AM provides deliverables in the form of archetypes and templates. An archetype provides the meta-description of structured clinical records as a computable formalism. In HL7's CDA, the two levels are the Reference Information Model (RIM) and the HL7 templates, which function essentially the same as the archetype concept.

These standards support compatibility among each other. In the case of openEHR and ISO/EN 13606, the only means of achieving interoperability with a generic information model is through their archetypes. In fact, ADL archetypes can be defined against any Unified Modeling Language (UML) model, and it is also possible to write the archetypes against the HL7 Version 3 RIM and the CDA in general. Kilic and Dogac [7] note this in their work, describing how the clinical statements of two different EHR standards derived from the same RIM can be mapped to each other by using archetypes, Refined Message Information Model derivations, and semantic tools.

As mentioned prior, an archetype is an agreed upon formal and interoperable specification of a re-usable clinical data set that underpins an EHR. It captures the maximum possible information about a particular and discrete clinical concept. [2] A conceptual definition of data as archetypes can be developed in terms of constraints on structure, types, values, and behaviors of reference model classes based on the dual-model approach. It consists of the knowledge layer as archetypes and a reference model. An example of a simple archetype is "Weight," which can be used in multiple places as required within an EHR.

Semantics in archetypes have a dual nature. They consist of both structural and terminological components. The structure of an archetype provides support for semantics, while EHR component links form a set of interrelated conceptual, clinical entities. Each entity has a set of terminological bindings associated with it (specified by links to terms of specific medical terminologies).

If data elements are created and modified using archetypes, the archetypes constrain the configuration of data instances to be valid according to the archetype. These are a paradigm for building semantically enabled software systems, providing data validation, clinical modeling (by domain experts), a basis for querying, and form design. An archetype might define or constrain relationships between data values within a data structure. These are expressed as algorithms, formula, or rules. An archetype's metadata defines its core concept, purpose, use, evidence, authorship, and versioning. An archetype also ensures a maximal dataset. It contains all the relevant information regarding a clinical concept. Once the format of an archetype is agreed upon and published, it is held in a "library’" and made available for use in any part of a given application by multiple vendor systems, multiple institutions, and multiple geographical regions. Each group or entity using the same archetype will understand and compute data captured by the same archetype in another clinical environment. Thus, an archetype serves the following key purposes [8,9]:

  1. It allows domain experts (clinicians) to capture data for their information systems.
  2. It provides runtime validation of data input, thus improving data entry quality.
  3. It provides a basis for intelligent querying of data.

Representing internal data in archetypes

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.