Can OpenEHR, ISO 13606, and HL7 FHIR Work Together? An Agnostic Approach for the Selection and Application of Electronic Health Record Standards to the Next-Generation Health Data Spaces.

In order to maximize the value of electronic health records (EHRs) for both health care and secondary use, it is necessary for the data to be interoperable and reusable without loss of the original meaning and context, in accordance with the findable, accessible, interoperable, and reusable (FAIR) principles. To achieve this, it is essential for health data platforms to incorporate standards that facilitate addressing needs such as formal modeling of clinical knowledge (health domain concepts) as well as the harmonized persistence, query, and exchange of data across different information systems and organizations. However, the selection of these specifications has not been consistent across the different health data initiatives, often applying standards to address needs for which they were not originally designed. This issue is essential in the current scenario of implementing the European Health Data Space, which advocates harmonization, interoperability, and reuse of data without regulating the specific standards to be applied for this purpose. Therefore, this viewpoint aims to establish a coherent, agnostic, and homogeneous framework for the use of the most impactful EHR standards in the new-generation health data spaces: OpenEHR, International Organization for Standardization (ISO) 13606, and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). Thus, a panel of EHR standards experts has discussed several critical points to reach a consensus that will serve decision-making teams in health data platform projects who may not be experts in these EHR standards. It was concluded that these specifications possess different capabilities related to modeling, flexibility, and implementation resources. Because of this, in the design of future data platforms, these standards must be applied based on the specific needs they were designed for, being likewise fully compatible with their combined functional and technical implementation.

Archetype modeling methodology.

Clinical Information Models (CIMs) expressed as archetypes play an essential role in the design and development of current Electronic Health Record (EHR) information structures. Although there exist many experiences about using archetypes in the literature, a comprehensive and formal methodology for archetype modeling does not exist. Having a modeling methodology is essential to develop quality archetypes, in order to guide the development of EHR systems and to allow the semantic interoperability of health data. In this work, an archetype modeling methodology is proposed. This paper describes its phases, the inputs and outputs of each phase, and the involved participants and tools. It also includes the description of the possible strategies to organize the modeling process. The proposed methodology is inspired by existing best practices of CIMs, software and ontology development. The methodology has been applied and evaluated in regional and national EHR projects. The application of the methodology provided useful feedback and improvements, and confirmed its advantages. The conclusion of this work is that having a formal methodology for archetype development facilitates the definition and adoption of interoperable archetypes, improves their quality, and facilitates their reuse among different information systems and EHR projects. Moreover, the proposed methodology can be also a reference for CIMs development using any other formalism.

Automatic generation of computable implementation guides from clinical information models.

Clinical information models are increasingly used to describe the contents of Electronic Health Records. Implementation guides are a common specification mechanism used to define such models. They contain, among other reference materials, all the constraints and rules that clinical information must obey. However, these implementation guides typically are oriented to human-readability, and thus cannot be processed by computers. As a consequence, they must be reinterpreted and transformed manually into an executable language such as Schematron or Object Constraint Language (OCL). This task can be difficult and error prone due to the big gap between both representations. The challenge is to develop a methodology for the specification of implementation guides in such a way that humans can read and understand easily and at the same time can be processed by computers. In this paper, we propose and describe a novel methodology that uses archetypes as basis for generation of implementation guides. We use archetypes to generate formal rules expressed in Natural Rule Language (NRL) and other reference materials usually included in implementation guides such as sample XML instances. We also generate Schematron rules from NRL rules to be used for the validation of data instances. We have implemented these methods in LinkEHR, an archetype editing platform, and exemplify our approach by generating NRL rules and implementation guides from EN ISO 13606, openEHR, and HL7 CDA archetypes.

Using the ResearchEHR platform to facilitate the practical application of the EHR standards.

Possibly the most important requirement to support co-operative work among health professionals and institutions is the ability of sharing EHRs in a meaningful way, and it is widely acknowledged that standardization of data and concepts is a prerequisite to achieve semantic interoperability in any domain. Different international organizations are working on the definition of EHR architectures but the lack of tools that implement them hinders their broad adoption. In this paper we present ResearchEHR, a software platform whose objective is to facilitate the practical application of EHR standards as a way of reaching the desired semantic interoperability. This platform is not only suitable for developing new systems but also for increasing the standardization of existing ones. The work reported here describes how the platform allows for the edition, validation, and search of archetypes, converts legacy data into normalized, archetypes extracts, is able to generate applications from archetypes and finally, transforms archetypes and data extracts into other EHR standards. We also include in this paper how ResearchEHR has made possible the application of the CEN/ISO 13606 standard in a real environment and the lessons learnt with this experience.

Using archetypes for defining CDA templates.

While HL7 CDA is a widely adopted standard for the documentation of clinical information, the archetype approach proposed by CEN/ISO 13606 and openEHR is gaining recognition as a means of describing domain models and medical knowledge. This paper describes our efforts in combining both standards. Using archetypes as an alternative for defining CDA templates permit new possibilities all based on the formal nature of archetypes and their ability to merge into the same artifact medical knowledge and technical requirements for semantic interoperability of electronic health records. We describe the process followed for the normalization of existing legacy data in a hospital environment, from the importation of the HL7 CDA model into an archetype editor, the definition of CDA archetypes and the application of those archetypes to obtain normalized CDA data instances.

Standardized and flexible health data management with an archetype driven EHR system (EHRflex).

To build a semantically interoperable Electronic Health Record is one of the most challenging research fields in health informatics. In order to reach this objective, EHR standards that formally describe health data structures have to be used. CEN EN13606 is one of the most promising approaches. It covers the technical needs for semantic interoperability and, at the same time, it incorporates a mechanism (archetype model) that enables clinical domain experts to participate in building an EHR system. In this paper we present EHRflex, a generic system based on archetypes. It empowers the clinician and allows him to manage his own EHR system in a simple and generic way, assuring that the user works with underlying standardized data structures. These can be exchanged with other people and systems when needed. EHRflex introduces EHR standards into the clinical routine delivering a technical platform which works directly on archetype based data.

ResearchEHR: use of semantic web technologies and archetypes for the description of EHRs.

In this paper, we present the ResearchEHR project. It focuses on the usability of Electronic Health Record (EHR) sources and EHR standards for building advanced clinical systems. The aim is to support healthcare professional, institutions and authorities by providing a set of generic methods and tools for the capture, standardization, integration, description and dissemination of health related information. ResearchEHR combines several tools to manage EHR at two different levels. The internal level that deals with the normalization and semantic upgrading of exiting EHR by using archetypes and the external level that uses Semantic Web technologies to specify clinical archetypes for advanced EHR architectures and systems.

CEN EN13606 normalisation framework implementation experiences.

Since the approval of the CEN EN13606 norm for the electronic health record communication, a growing interest around the application of this specification has emerged. The main objective of the norm is to serve as a mechanism to achieve the semantic interoperability of clinical data. This will require an effort to use common terminologies, to normalise the clinical knowledge domain and to combine all these formalisations with the existing information systems. This paper presents a methodology and developed tools to reach the seamless semantic interoperability of health data in legacy systems and several study cases where the developed framework has been applied.

Framework for clinical data standardization based on archetypes.

Standardization of data is a prerequisite to achieve semantic interoperability in any domain. This is even more important in the healthcare sector where the need for exchanging health related data among professional and institutions is not an exception but the rule. Currently, there are several international organizations working on the definition of electronic health record architectures, some of them based on a dual-model approach. We present both an archetype modeling framework and LinkEHR-ED, an archetype editor and mapping tool for transforming existing electronic healthcare data which do not conform to a particular electronic healthcare record architecture into compliant electronic health records extracts. In particular, archetypes in LinkEHR-ED are formal representations of clinical concepts built on a particular reference model but enriched with mapping information to data sources which define how to extract and transform existing data in order to generate standardized XML documents.

A Standardized and Data Quality Assessed Maternal-Child Care Integrated Data Repository for Research and Monitoring of Best Practices: A Pilot Project in Spain.

We present the results of a pilot project of the Spanish Ministry of Health, Social Services and Equality, envisaged to the development of a national integrated data repository of maternal-child care information. Based on health information standards and data quality assessment procedures, the developed repository is aimed to a reliable data reuse for (1) population research and (2) the monitoring of healthcare best practices. Data standardization was provided by means of two main ISO 13606 archetypes (composed of 43 sub-archetypes), the first dedicated to the delivery and birth information and the second about the infant feeding information from delivery up to two years. Data quality was assessed by means of a dedicated procedure on seven dimensions including completeness, consistency, uniqueness, multi-source variability, temporal variability, correctness and predictive value. A set of 127 best practice indicators was defined according to international recommendations and mapped to the archetypes, allowing their calculus using XQuery programs. As a result, a standardized and data quality assessed integrated data respository was generated, including 7857 records from two Spanish hospitals: Hospital Virgen del Castillo, Yecla, and Hospital 12 de Octubre, Madrid. This pilot project establishes the basis for a reliable maternal-child care data reuse and standardized monitoring of best practices based on the developed information and data quality standards.

Combining Archetypes with Fast Health Interoperability Resources in Future-proof Health Information Systems.

Messaging standards, and specifically HL7 v2, are heavily used for the communication and interoperability of Health Information Systems. HL7 FHIR was created as an evolution of the messaging standards to achieve semantic interoperability. FHIR is somehow similar to other approaches like the dual model methodology as both are based on the precise modeling of clinical information. In this paper, we demonstrate how we can apply the dual model methodology to standards like FHIR. We show the usefulness of this approach for data transformation between FHIR and other specifications such as HL7 CDA, EN ISO 13606, and openEHR. We also discuss the advantages and disadvantages of defining archetypes over FHIR, and the consequences and outcomes of this approach. Finally, we exemplify this approach by creating a testing data server that supports both FHIR resources and archetypes.

Archetype-based data warehouse environment to enable the reuse of electronic health record data.

BACKGROUND: The reuse of data captured during health care delivery is essential to satisfy the demands of clinical research and clinical decision support systems. A main barrier for the reuse is the existence of legacy formats of data and the high granularity of it when stored in an electronic health record (EHR) system. Thus, we need mechanisms to standardize, aggregate, and query data concealed in the EHRs, to allow their reuse whenever they are needed. OBJECTIVE: To create a data warehouse infrastructure using archetype-based technologies, standards and query languages to enable the interoperability needed for data reuse. MATERIALS AND METHODS: The work presented makes use of best of breed archetype-based data transformation and storage technologies to create a workflow for the modeling, extraction, transformation and load of EHR proprietary data into standardized data repositories. We converted legacy data and performed patient-centered aggregations via archetype-based transformations. Later, specific purpose aggregations were performed at a query level for particular use cases. RESULTS: Laboratory test results of a population of 230,000 patients belonging to Troms and Finnmark counties in Norway requested between January 2013 and November 2014 have been standardized. Test records normalization has been performed by defining transformation and aggregation functions between the laboratory records and an archetype. These mappings were used to automatically generate open EHR compliant data. These data were loaded into an archetype-based data warehouse. Once loaded, we defined indicators linked to the data in the warehouse to monitor test activity of Salmonella and Pertussis using the archetype query language. DISCUSSION: Archetype-based standards and technologies can be used to create a data warehouse environment that enables data from EHR systems to be reused in clinical research and decision support systems. With this approach, existing EHR data becomes available in a standardized and interoperable format, thus opening a world of possibilities toward semantic or concept-based reuse, query and communication of clinical data.

Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis.

OBJECTIVE: This systematic review aims to identify and compare the existing processes and methodologies that have been published in the literature for defining clinical information models (CIMs) that support the semantic interoperability of electronic health record (EHR) systems. MATERIAL AND METHODS: Following the preferred reporting items for systematic reviews and meta-analyses systematic review methodology, the authors reviewed published papers between 2000 and 2013 that covered that semantic interoperability of EHRs, found by searching the PubMed, IEEE Xplore, and ScienceDirect databases. Additionally, after selection of a final group of articles, an inductive content analysis was done to summarize the steps and methodologies followed in order to build CIMs described in those articles. RESULTS: Three hundred and seventy-eight articles were screened and thirty six were selected for full review. The articles selected for full review were analyzed to extract relevant information for the analysis and characterized according to the steps the authors had followed for clinical information modeling. DISCUSSION: Most of the reviewed papers lack a detailed description of the modeling methodologies used to create CIMs. A representative example is the lack of description related to the definition of terminology bindings and the publication of the generated models. However, this systematic review confirms that most clinical information modeling activities follow very similar steps for the definition of CIMs. Having a robust and shared methodology could improve their correctness, reliability, and quality. CONCLUSION: Independently of implementation technologies and standards, it is possible to find common patterns in methods for developing CIMs, suggesting the viability of defining a unified good practice methodology to be used by any clinical information modeler.

Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.

BACKGROUND: The secondary use of electronic healthcare records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, virtual health records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. OBJECTIVE: To develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. MATERIALS AND METHODS: We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (ie, data level) and the rest using ontologies (ie, knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. RESULTS: We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies, and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data have been used and the patients have been successfully classified by the risk of developing colorectal cancer. CONCLUSIONS: This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies, and classification rules can be designed.

LinkEHR-Ed: a multi-reference model archetype editor based on formal semantics.

PURPOSE: To develop a powerful archetype editing framework capable of handling multiple reference models and oriented towards the semantic description and standardization of legacy data. METHODS: The main prerequisite for implementing tools providing enhanced support for archetypes is the clear specification of archetype semantics. We propose a formalization of the definition section of archetypes based on types over tree-structured data. It covers the specialization of archetypes, the relationship between reference models and archetypes and conformance of data instances to archetypes. RESULTS: LinkEHR-Ed, a visual archetype editor based on the former formalization with advanced processing capabilities that supports multiple reference models, the editing and semantic validation of archetypes, the specification of mappings to data sources, and the automatic generation of data transformation scripts, is developed. CONCLUSIONS: LinkEHR-Ed is a useful tool for building, processing and validating archetypes based on any reference model.

Semantic upgrade and normalization of existing EHR extracts.

We present the mapping and data transformation capabilities of LinkEHR-Ed, a visual tool to construct formal definitions of medical concepts in the form of archetypes which can be defined on the basis on multiple electronic health record architecture such as ISO 13606. With LinkEHR-Ed, users can enrich archetypes with mapping information which captures the relationship between relational or XML data sources and archetype structures. This mapping information is then analyzed and compiled into an XQuery expression that transforms source instances into an XML document. The target document satisfies the constraints imposed by the archetype and at the same time is compliant with the underlying electronic health record architecture.

Standardization of discharge reports with the ISO 13606 norm.

The International Organization for Standardization (ISO) has recently approved a new standard for the communication and semantic interoperability of electronic health record extracts. This standard is based on a dual model architecture, where a simple and generic reference model is defined for the representation of data and an archetype model is used for the representation of complex domain concepts of the electronic health record. By using this standard and a tool called LinkEHR-Ed, we have defined different types of hospital discharge reports in the form of archetypes and then we have normalized automatically discharge reports instances in a real environment following those archetype definitions. This work proves that it is possible to standardize legacy data automatically and enrich them with a semantic information layer by using archetypes as an integration and standardization mechanism.

A Semantic Web-based System for Managing Clinical Archetypes.

Archetypes facilitate the sharing of clinical knowledge and therefore are a basic tool for achieving interoperability between healthcare information systems. In this paper, a Semantic Web System for Managing Archetypes is presented. This system allows for the semantic annotation of archetypes, as well for performing semantic searches. The current system is capable of working with both ISO13606 and OpenEHR archetypes.

Non-invasive lightweight integration engine for building EHR from autonomous distributed systems.

In this paper we describe Pangea-LE, a message-oriented lightweight data integration engine that allows homogeneous and concurrent access to clinical information from disperse and heterogeneous data sources. The engine extracts the information and passes it to the requesting client applications in a flexible XML format. The XML response message can be formatted on demand by appropriate Extensible Stylesheet Language (XSL) transformations in order to meet the needs of client applications. We also present a real deployment in a hospital where Pangea-LE collects and generates an XML view of all the available patient clinical information. The information is presented to healthcare professionals in an Electronic Health Record (EHR) viewer Web application with patient search and EHR browsing capabilities. Implantation in a real setting has been a success due to the non-invasive nature of Pangea-LE which respects the existing information systems.

Archetype-based semantic integration and standardization of clinical data.

One of the basic needs for any healthcare professional is to be able to access to clinical information of patients in an understandable and normalized way. The lifelong clinical information of any person supported by electronic means configures his/her Electronic Health Record (EHR). This information is usually distributed among several independent and heterogeneous systems that may be syntactically or semantically incompatible. The Dual Model architecture has appeared as a new proposal for maintaining a homogeneous representation of the EHR with a clear separation between information and knowledge. Information is represented by a Reference Model which describes common data structures with minimal semantics. Knowledge is specified by archetypes, which are formal representations of clinical concepts built upon a particular Reference Model. This kind of architecture is originally thought for implantation of new clinical information systems, but archetypes can be also used for integrating data of existing and not normalized systems, adding at the same time a semantic meaning to the integrated data. In this paper we explain the possible use of a Dual Model approach for semantic integration and standardization of heterogeneous clinical data sources and present LinkEHR-Ed, a tool for developing archetypes as elements for integration purposes. LinkEHR-Ed has been designed to be easily used by the two main participants of the creation process of archetypes for clinical data integration: the Health domain expert and the Information Technologies domain expert.