Semantic Interoperability In Healthcare Research Articles

How ontology can be used to achieve semantic interoperability in healthcare

Abstract Semantic interoperability allows machines to share, interpret and use data without ambiguity. Semantic Interoperability is a major concern in healthcare (c.f. the EU commission 2021 report on electronic record exchange formats). The lack of interoperability with regard to electronic health record (EHR) leads to fragmentation and a lower quality of cross-institution and cross-border healthcare. The simple choice of an interchange language (HL7, FIHR etc.)is not sufficient to ensure interoperability. Healthcare interoperability is associated with multi-level and multi-sectoral complexity, and this cannot be addressed without consideration of a range of people and needs, from application design to knowledge sharing. Each transaction needs to be defined in unambiguous details as part of a complete, consistent, coherent, and machine-readable set of specifications for interoperability between the machines to minimize any potential error. We propose a systematic process to achieve healthcare interoperability, working with healthcare professionals starting from design level to implementation level. In our seminar we will explain with examples how ontology can be used to achieve semantic interoperability in healthcare. Technical requirements, including the choice of tools (e.g. Protégé); data base (e.g. GraphDB); data model (i.e. Web Ontology Language 2 (OWL2); formal specification (i.e. Description logics (DL)); and the right syntax (RDF/XML). Will be introduced.

Commoning Semantic Interoperability in Healthcare

Commons traditionally refer to shared natural resources that are at risk of being depleted or even destroyed. The rules established by commoners offer a way to manage such scarce resources. Through a series of projects on national eHealth patient health records, we have identified another type of shared resources – semantic resources – that would benefit from commons-based governance. Semantic resources are standardised definitions of data that ensure a common understanding of data during processes of data sharing between different actors and systems. In this paper, we first use commons as a lens to explore shared semantic resources and the governance challenges associated with them (e.g. lack of cross-cutting support across many projects). This leads to a better understanding of (1) shared semantic resources as global commons, (2) the relationship between the global and local levels of semantic resource governance and, by observing traces of commoning at the local level, (3) the role of commoning as a crucial concept in the context of semantic interoperability. Second, we introduce the notion of semantic commons to address the governance problems of semantic resources that needs to be continuous and sustainable. In addition, we also contribute to the commons literature by proposing a new area of research for commons scholars. We identify semantic resources in healthcare as an interesting and important type of resource that offers a new perspective on the dynamics of the relationship between global and local commons. We also identify a central role for commoning in healthcare in general as well as in the governance of semantic resources there.

Semi-Automated Approach to Map Clinical Concepts to SNOMED CT Terms by Using Terminology Server.

SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. Terminology Servers provide an interface, which can be used to automate the process of retrieving data. Snowstorm is a terminology server developed by SNOMED International. In this work, the feasibility of using Snowstorm to automate the data retrieval and mapping has been discussed.

Infrastructure and Capacity Building for Semantic Interoperability in Healthcare in the Netherlands.

Over 15 years, a broad spectrum of activities was undertaken to realize a health IT infrastructure in the Netherlands. In this paper we reflect on the history, challenges, accomplishments, changes, and the way forward. It shows that the infrastructure depends on technical, legal, and semantic aspects, which are frequently reciprocally related. It also highlights the fact that the role of health professionals and of patients is increasingly considered as a crucial element.

Semantic interoperability of controlled vocabularies in medicine: A case study of the International Statistical Classification of Diseases ‘Tuberculosis’ subset

Controlled vocabularies have been promoted for the achievement of semantic interoperability in e-health. However, the current implementations of healthcare information systems struggle with the remaining encoded semantics inside each particular data model. Multilevel modelling has been proposed for overcoming the challenges of semantic interoperability in healthcare, with different approaches for binding domain models to standard vocabularies; however, proofs of concept are still needed. This paper presents the fundamentals of knowledge modelling with standard vocabularies using the Multilevel Healthcare Information Modelling (MLHIM) specifications. The implementation of one term subset (‘Tuberculosis’) of the 10th Revision of the International Classification of Diseases (ICD-10) in MLHIM domain models, the Concept Constraint Definitions (CCD), is described, using the Brazilian mortality and hospital information systems as use cases. Technical details of the semantic validation of data instances generated according to the XML instances that include this ICD-10 term set, as well as the correspondent migration of the original databases to MLHIM-compliant databases, are presented.

Semantic interoperability in healthcare.

The improved understanding of the whole healthcare and wellbeing pathway for individuals depends on the power of healthcare information, being made readily available and easily interpretable by both humans and machines. In the centre of this challenge lies the concept of interoperability, as a way to provide fundamental linkage, integration and meaningful use of healthcare data between systems, organisations and users. In particular, semantic interoperability can offer a way of enriching healthcare data with context and meaning, in order to achieve enhanced understanding and better evidence-based interpretation of disease and wellbeing for an individual, within the context of healthcare provision and clinical research. In this keynote paper, the concept of semantic interoperability, for healthcare data linkage and exchange, is critically discussed. An emphasis is given on the importance of clinical terminologies and associated services, as means for adding meaning to routinely collected clinical data.

Interoperability for medical information systems: an overview

As it is known to all, there is an imperative need for electronic transfer of healthcare information (patients’ records, laboratory analyses, medical images…) between healthcare organizations to improve the quality level they are offering. However, medical information systems in each organization are different from each other. These different systems compose a heterogeneous, distributed system with high complexity: there are a large number of healthcare related applications that support specific needs effectively but are isolated or incompatible. Thus medical information systems’ interoperability constitutes one of the main challenges of the IT society. Semantic interoperability in healthcare is important especially when all the varying types of data need to interact. Presented in this paper are the most relevant works to our knowledge, dealing with interoperability in medical information systems and a comparative study between the new proposed approaches and research trends to resolve heterogeneity issues.

OWL-based reasoning methods for validating archetypes

Some modern Electronic Healthcare Record (EHR) architectures and standards are based on the dual model-based architecture, which defines two conceptual levels: reference model and archetype model. Such architectures represent EHR domain knowledge by means of archetypes, which are considered by many researchers to play a fundamental role for the achievement of semantic interoperability in healthcare. Consequently, formal methods for validating archetypes are necessary. In recent years, there has been an increasing interest in exploring how semantic web technologies in general, and ontologies in particular, can facilitate the representation and management of archetypes, including binding to terminologies, but no solution based on such technologies has been provided to date to validate archetypes. Our approach represents archetypes by means of OWL ontologies. This permits to combine the two levels of the dual model-based architecture in one modeling framework which can also integrate terminologies available in OWL format. The validation method consists of reasoning on those ontologies to find modeling errors in archetypes: incorrect restrictions over the reference model, non-conformant archetype specializations and inconsistent terminological bindings. The archetypes available in the repositories supported by the openEHR Foundation and the NHS Connecting for Health Program, which are the two largest publicly available ones, have been analyzed with our validation method. For such purpose, we have implemented a software tool called Archeck. Our results show that around 1/5 of archetype specializations contain modeling errors, the most common mistakes being related to coded terms and terminological bindings. The analysis of each repository reveals that different patterns of errors are found in both repositories. This result reinforces the need for making serious efforts in improving archetype design processes.

Ontology Matching: An Ultimate Solution for Semantic Interoperability in Healthcare

healthcare domain is a complex domain which lacks a unified terminological set, most especially in clinical cases. As a result of this, the messaging standards employed in the healthcare domain use different terms for the same concept which often results in clinical misinterpretation, knowledge mismanagement, misdiagnosis of the patient's illness or even death. Consequently, the healthcare system is characterized by high error rate and semantic heterogeneity. A lot of efforts have been made to resolve this problem through the use of standards, clinical terminologies, web services as well as the use of achetype. However, these solutions have proved unsuccessful in resolving semantic heterogeneity in healthcare. Ontologies have also been developed to resolve this problem by making explicit the meaning of terms used in healthcare. Ontologies provide a source of shared and precisely defined terms, resulting in interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that the healthcare domain is conceptualized results in the creation of different ontologies with contradicting or overlapping parts. Thus, the available ontologies also introduce semantic heterogeneity to this domain. An effective solution to this problem is the introduction of methods for finding matches among the various components of ontologies in healthcare in order to facilitate semantic interoperability. Therefore, this paper aims at examining the various attempts for achieving semantic interoperability in healthcare and also motivates the critical needs for ontology matching in healthcare systems. General Terms Ontology, Interoperability, Semantic. Keywordsntology, ontology matching, semantic heterogeneity, interoperability, semantic interoperability.

A logical approach to semantic interoperability in healthcare.

Singapore is in the process of rolling out a number of national e-health initiatives, including the National Electronic Health Record (NEHR). A critical enabler in the journey towards semantic interoperability is a Logical Information Model (LIM) that harmonises the semantics of the information structure with the terminology. The Singapore LIM uses a combination of international standards, including ISO 13606-1 (a reference model for electronic health record communication), ISO 21090 (healthcare datatypes), and SNOMED CT (healthcare terminology). The LIM is accompanied by a logical design approach, used to generate interoperability artifacts, and incorporates mechanisms for achieving unidirectional and bidirectional semantic interoperability.

Revolutionary impact of XML on biomedical information interoperability

The use of Extensible Markup Language (XML) to implement data sharing and semantic interoperability in healthcare and life sciences has become ubiquitous in recent years. Because in many areas there was no preexisting data format, XML has been readily embraced and is having a great impact. Biomedical data is very heterogeneous, varying from administrative information to clinical data, and recently to genomic data, making information exchange a great challenge. In particular, it is hard to achieve semantic interoperability among disparate and dispersed systems—a common constellation in the fragmented world of healthcare. Moreover, the emerging patient-centric and information-based medicine approach is posing another challenge—the development and use of an integrated health record for each patient. This means that diverse data from many systems has to be generated, integrated, and become available at the point of care. This paper presents the case that XML is becoming the integration “glue” for biomedical information interoperability, which can lead to improvements in pharmaceuticals, genomic-based clinical research, and personalized medicine, which, for the first time, can be fine-tuned to serve individuals through their longitudinal electronic health records.