Service and Model-Driven Dynamic Integration of Health Data Adel Taweel
Stuart Speedie
Gareth Tyson
Department of Informatics Kingʼs College London London, WC2R 2LS, UK
Health Informatics University of Minnesota USA
Department of Informatics Kingʼs College London London, WC2R 2LS, UK
[email protected]
[email protected]
[email protected]
A R Hani Tawil
Kevin Peterson
Brendan Delaney
School of Architecture, Computing & Engineering University of East London, UK
Family Medicine University of Minnesota USA,
Depart of Primary care Kingʼs College London London, SE1 3QD, UK
[email protected]
[email protected]
[email protected]
that patient health information is available at the point of care irrespective of its location. This expectation is driven by rapid developments in information technology, alongside their increasing dominance at the heart of individual healthcare institutions through the use of data-intensive electronic health records systems (EHRs). However, there are several challenges to overcome before such integrated health enterprise can be realised; these include:
ABSTRACT Recent advances in informatics and computing are increasingly becoming essential factors for realising major improvement in healthcare. The delivery of the right information about the right patient at the point of care is central to a well-integrated healthcare process. However, the high sensitivity of the clinical domain and the vast differences in health data and systems pose a great interoperability challenge for solutions that do not employ strong semantic principles as core to the interoperation process to sufficiently scale. This paper presents a model-based approach that utilises domain ontologies combined with extensible problem models, driven by rich domain terminology and knowledge services at the centre of the process to enable autonomous data governance and semantic interoperability. The paper addresses the resulting requirements and proposes a solution outlining the results from the prototype of the approach.
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Categories and Subject Descriptors
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D.2.12 Interoperability: Data mapping; J.3 [Computing Applications]: Life and Medical Sciences — Medical information systems;
the heterogeneity of eHealth systems and their data representations across health institutions the delivery of clinical information at the point of care, potentially requires health information exchange across numerous wide geographically distributed health centres, thus creating a massive scalability challenge. The lack of or slow pace of adoption of standards make exchanging clinical data more difficult and hampers the chances of integrated healthcare. the wide variation of data governance policies primarily aimed at maintaining patient privacy and confidentiality across health institutions.
The most serious challenges to realising an integrated healthcare system are however not only technological but ethical and structural. The large number of system providers and their business models and variations in ethical and regulations for accessing clinical information across the numerous involved health organisations implies an exponential explosion in the number and type of stakeholders that need to be involved in achieving the seamless data integration required to achieve such system. Therefore, to accomplish this we need to enable interoperation between data centres not just at the system or data levels but also at the policy level. However, given the rare usage and slow adoption of standard-based approaches, the effort required and complexity involved in reaching agreements or enforcing conformance at these different levels within a single enterprise let alone multiple countries is insurmountable. Thus, the approach taken here is motivated by the practical implications of not being able to completely rely on the use of common interoperability standards to address this issue. Instead, this needs to be addressed at the granularity of the application, data and/or centre levels, in which an interoperability framework should
General Terms: Design Keywords: Interoperability, ehealth, semantic web services, ontology, data integration.
1. INTRODUCTION The need for integrated systems that can provide up-to-date information about patients and their healthcare is not only critical to the day-to-day running and delivery of health functions but also for potentially saving lives [13, 6, 14, 15]. This requires welllinked health organisations that act as an integrated body. This is driven by current health needs, where it is increasingly expected Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. MIXHS’11, October 28, 2011, Glasgow, Scotland, UK. Copyright 2011 ACM 978-1-4503-0954-7/11/10...$10.00.
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overcome heterogeneity and adapt at run-time to dynamically interoperate with data providers. Heterogeneity between health systems and data, however, has several dimensions; the most challenging of which is the semantic one, which the approach proposed in this paper attempt to address.
closed domains, focusing on health. To do so, we propose that the service-oriented approach must be supplemented by a (run-time) computationally interpretable domain-based semantic representation, for both the data and its access constraints.
3. SEMANTIC INTEROPERABILITY AND INTEGRATION
In this paper, we focus on health data sharing in the context of clinical research and in the context of health organisations that use electronic health records systems (EHRs) through which patient health data is managed and stored. The paper proposes the use of ‘problem’ models supported with well-defined and controlled domain vocabulary that can be dynamically translated into individual data sources. Data sources are linked and made available through the use of web services that are semantically described in terms of a domain ontology [10], which is dynamically discovered and bound to at run-time.
Healthcare systems are typical information systems. Due to the large and complex nature of the Health domain functions in various countries and the ways they are provided, a typical health organization can have several different information systems, ranging from diagnostic medical devices to electronic health record systems (EHRs), which is the focus of this paper. EHRs hold patient health record information and support the delivery of day-to-day care in hospitals in speciality and primary care clinics. The concern for the project upon which this paper is based (ePCRN: electronic Primary Care Research Network) was to leverage the use of EHRs, from primary care clinics, to support clinical research. Due to various historic reasons, such clinics use many different EHR systems with varying degrees of complexity. This heterogeneity has resulted in significant differences across EHRs in terms of their data representations, let alone communication and interoperability between them. Interoperability complexities occur in several forms [12, 7, 4]: these include system, syntactic, structural semantic, and security complexities. Various methods have been suggested to resolve system and syntactic interoperability problems, which are often easier to resolve [12, 7]. However, achieving structural and semantic interoperability, for example, in information interrogation or interchange between information systems continues to be a difficult problem. In order to achieve semantic interoperability in a heterogeneous environment, the meaning of the information that is interrogated (or communicated) has to have the same semantics or meaning across the systems. In clinical systems, the complexity is in the ways that different systems represent their medical information. For example, references to clinical procedures, problems or diagnosis and the way each system records or codes each concept. Equally, different systems use different codings to refer to or represent their data, such as SNOMED-CT, Read Codes, ICD9 etc.
2. CHALLENGES OF INTEROPERABILITY IN THE HEALTH DOMAIN Service-oriented architectures share many of the challenges that are faced by health systems. However, the focus in health is on the data itself and less on the functionality. In principle, however, these challenges are similar and include: using common interoperability mechanisms to integrate services from a large number of disparate health data sources; separating the interoperability of functionality from that of data (in this case the main element is the heterogeneity of the data); and binding dynamically at run-time to systems without prior detailed knowledge of their inner details. Therefore the paper proposes a domain focussed interoperability representation that utilises service-oriented architecture technologies. In other words, to define the service-oriented interoperability mechanism based on the clinical data that constitutes the heart of the services. In a complex and closed domain, such as health, where the function of services isare defined, the interoperability mechanism is enabled through a domain-specific semantic representation or ontology that provides the required richness and accuracy. Basing the interoperability mechanism within services, using a domain-specific representation provides the required extensible richness and complexity needed in the domain. This is opposed to having a one for all approach or incorporating it in the functional interface, thus over-complicating or limiting its extensibility respectively. Instead, this approach makes the architecture potentially re-usable across other domains that require such requirements.
In the context of the clinical research domain and linkage to EHRs, for the main requirements, a number of capabilities will be needed: provide a secure and coherent view of the data from different autonomous heterogeneous systems or data sources at the point of access, maintain and allow independent and different data access and sharing policies, allow independent changes of data source or systems, achieve transparent or seamless integration or interrogation of data sources. These capabilities require a flexible framework that enables dynamic binding or access-on-demand to the data sources or EHR systems through an interoperable integration framework. The need is not only to enable configurable yet rich semantic descriptions of these systems, but also the underlying data that they serve. Such descriptions are not currently supported in current web services standards or approaches[1].
A domain-based service-oriented architecture for interoperability in healthcare presents additional unique challenges. For instance, data sources can be large and complex, served by autonomous, slowly evolving legacy systems. Data sources reside on different and varied platforms and their records can potentially range from several thousands to hundreds of thousands, if not millions. Many have low-bandwidth access and all are used to directly support the healthcare process and are potentially already overloaded and thus accessing them in real-time by other services needs to be taken under strict conditions. This effectively limits available processing capabilities and offers differing reliability when compared to dedicated function-focused Internet web services. However, for the purposes of this paper, we will be focusing on interoperable semantic composition in the health domain. In particular, we aim to address semantic interoperability and the pressures placed on the service engineering process through the use of semantic representation in data-intensive services in complex, sensitive and
Semantic models (often represented as ontologies) can be used to describe the semantics of health systems or resources and make the content explicit, and thus can be used to discover semantic equivalence between information concepts. The use of shared or standard ontologies as a possible solution to the semantic interoperability problem has been studied for over a decade. They
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have also been found to provide reasonable results for semantic interoperability [17, 3, 18]. In a market place setting, where terminology is not well defined and varied, there will be a need to provide users with a freedom of choice in terms of how they structure their concepts or semantics and their preferred representation language. However, this will be difficult to enforce and thus will, in turn, lead to what is often referred to as the ontology heterogeneity problem [16, 5]. This may make it difficult to achieve semantic interoperability as intended. However, given the closed domain, this can be avoided by standardizing the models that can be used to describe data as well as the service. Equally, this domain model (or ontology) needs to be clear enough and to employ an agreed format to be used by data providers to describe their data in a meaningful way. In the ePCRN project, we built our ontology based on the CCR standard (a variation of the widely adopted HL7) to represent the core medical domain concepts for primary care.
enrichment, domain vocabulary and knowledge, dynamic model
4. MODEL AND SERVICE BASED APPROACH The health enterprises in many countries are often made up of independent organizations and institutions providing healthcare services to its patients or customers, forming often isolated yet loosely interconnected organizations within the enterprise – Health Service (HS). Across the domain, they have their own rich and complex data representations, terminologies and acronyms that are represented in real terms by the health systems that support these organizations. While services provide a potential solution to enable interoperability between the organizations and their systems, the focus needs to be on sufficiently describing the data, and to a lesser extent the systems themselves, opposed to the functional services that enable access to them.
Figure 1: Overall Architecture transformations, and dynamic discovery and binding of services (or data sources) are some of the main functions provided by the middle. Problem models are captured in the top layer and enriched with appropriate conforming domain vocabulary and knowledge through the middle layer. The bottom layer includes the services that provide access to the actual data sources. Services register themselves at the meta-registry to include a self-description of their meta-data and local data representation at the point of configuration. Data representation of data sources are described using a common data element representation based on the 11179 standard and linked with a respective domain terminology. This enables problem models –to-local data source instance translation at the middle layer dynamically, on demand, at the point of query.
This is the approach taken in the ePCRN project. The ePCRN (web or grid) services composition architecture includes a rich semantically annotated representation of the ‘problem’ or the ‘user data requirement’ model and an enactment broker that interoperates with health systems using information from a meta registry that holds the domain ontology and mapping to health systems. As illustrated in Figure 1, in the architecture, referred to as ePCRN-IIA (Interoperable Integration Architecture), includes five main semantic elements to achieve this objective: terminology and knowledge services that provide rich domain terminologies, referred to as Knowledge Services; a Meta Registry that holds rich metadata descriptions of services (i.e. physical resources or health systems); ‘Problem’ models referred to as Extensible Query Abstraction Model (EQAM); domain and data governance models referred to as Extensible Domain Data Abstraction Ontology (EDAO); and Extensible Data Governance Abstraction Ontology (EDGAO) and the mapping between them. The latter three are centrally stored in the Model/Ontology Service (see Figure 1).
The EQAM is a model representation that allows end users to capture their data needs in simple or reasonably complex logical constructs based on vocabulary controlled clinical concepts enriched by the terminology knowledge resources or knowledge services. Each EQAM instance represents a semantically annotated set of logically combined clinical concepts that can be translated into other more specific formats. We refer to this as the problem model, in which users specify their specific data requirements or characteristics. For example, for clinical trials, this model can represent the eligibility criteria of a trial; for clinical care, it can represent the combination of data elements that need to be retrieved. An example of an eligible criteria model to support clinical trials in primary care is illustrated in Figure 2.
The general approach is to capture the ‘problem’ in a semantically annotated extensible model, which can be passed across the infrastructure and translated to or by individual services or EHRs using the domain and mapping ontology while respecting individual data governance policies, thus creating a semanticallyaware architecture. The function of each of these elements is described below in more details.
The domain ontology concepts are mapped to the local health system service data using a mapping ontology, which is a configurable XML representation that can be edited by the users as systems evolve. It also includes the used terminology for each class, their versions and the way they are used in coding their values. For example, the domain ontology describes Problems, Medications, Tests, Procedures etc. and their attributes and relationships. Domain ontology concepts are described or represented using data element. Individual data elements with appropriate linked relationships form a collection, which are
The architecture uses three main layers, with the middle layer including knowledge and terminology services acting as central connecting ‘heart’ of the framework. Communication, model
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described using 11179, as mentioned above. The mapping ontology describes the corresponding concepts or attributes in the local health system with those in problem model. The attributes that correspond to each class may come from different locations, or tables in the local health system. Each class may be described by a different terminology, medical vocabulary or coding system, such as SNOMED-CT, Read Codes etc. This ontology is used by the ePCRN-IIB (Interoperable Integration Broker) services, in this case the Query Generator Service (QGS), to translate the EQAM into one or several queries that correspond to the respective local health system to query or retrieve the respective data. The Query Enactor Service (QES) is responsible for enacting and assembling the output. Figure 2 illustrates the capture, translation and execution sequence of the models. At the health system side, the execution is governed by the Data Governance ontology, which describes the rules that the service should enforce to control access to the data in the health systems. These rules are defined as policy rules in terms of roles and their access privileges.
middleware and the data services. This mechanism was implemented as part of the first ePCRN prototype. In the early prototypes, it was assumed that data services would have the same functional interface, thus the metadata definition of the services was limited in scope by the implemented interface. With the expansion of the ePCRN network it was inevitable that data services would change in versions as EHR systems evolve. To enable this evolution of services, the metadata definition model of the services was redeveloped to become more extensible. As a consequence, this enabled the used transformation or bridging mechanism to interface with different types of data services that have different functional interface or data representations, thus potentially enabling the ePCRN network to communicate with other data services from different compatible networks.
5. DYNAMIC GENERATION OF SEMANTIC INTEROPERABLE DOMAIN CONCEPTS AND DEFINITIONS To enable semantic translations between services (or data sources) and the defined problem models, a mechanism for bridging between the two is needed. This bridging mechanism includes translations at two levels: at the problem models level, definition annotated by the knowledge services’ terminologies, and at the services’ levels translated through the Query Generation Service. Figure 3 illustrates the process of the problem model definitions through bridging between models and the services. Figure 4 illustrates an interface to find and retrieve clinical concepts from a knowledge resource. The Domain Concept Mapping Service (DCMS, referred to as EVSInterface in Figure 2) works across different ontologies (or concept definitions returned from each knowledge service) and aims to dynamically produce the richest possible definition for each requested domain concept. Each clinical concept definition returned from each knowledge service is analysed for a possible mapping route from the returned definition. If a mapping route is found, as illustrated in Figure 5, the respective knowledge services that contain extra mappings or definitions are accessed and the returned data is added to the problem model, i.e. EQAM. These additional mappings are annotated separately for the user and the QG Service exclusion if needed. Figure 4 illustrates an example of producing a rich definition of related domain concepts. Each domain concept definition includes references to other domain ontologies enabling multiple rich definitions of each domain concepts. These multiple definition domain concepts from different ontologies enables the mapping service to dynamically use the appropriate definition map supported in the local data source instance.
Figure 2: EQAM - An Example of a Generic Eligibility Criteria Model
The Meta Registry combines the knowledge about services (i.e. physical resources or health systems and their descriptions), the domain ontology capturing the relevant domain concepts (focusing on primary care) and the mappings between the two. It also holds the services’ corresponding data governance policies. It acts as a central (domain) description service that holds functional descriptions of the data services themselves, and descriptions of the underlying health data that they provide access to, alongside a description of the access mechanism or rules that governs access to the data. For service description, it uses a WSDL form for service meta function description, each linked with the above ontology description for each data source.
The QG Service performs the second bridging or translation function between the captured EQAM instances and the data services using service metadata, domain and data governance ontologies of the respective services from the meta-registry. The exact format of the QG service output depends on several factors, including the data schema representation, terminologies used, data storage mechanism and the data service functional interface definition.
This approach maintains the semantic-awareness of each domain concept in the problem model across the different layers of the system, from the top end user level through to the system
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components to employ semantically rich problem models combined with domain ontologies, supported by knowledge services. The problem models, EQAM, combined with the terminology and knowledge services are the backbone of this approach to enable the semantic interoperability in the current prototype. They enabled semantic-awareness through the architecture to individual data sources. However, the richness of the semantic-awareness in the architecture depends, to a great extend, on the richness of the terminologies and knowledge services. While there always will potentially be limitations of the richness of terminologies and their mappings, the key is to enable observing and recognising it in the respective data services. There needs to exist a feedback mechanism to recognise and quantify semantic shortcomings where they exist. Given the legacy nature of electronic health records, it would be difficult to assume completeness in the knowledge services that include all terminologies. However, the key is that this approach enables extensibility, allowing the addition of terminologies while maintaining the integrity of the results of problem model instances, which is critical in the clinical domain.
Figure 3: Dynamic generation of semantically interoperable defined domain concepts
6. PRELIMINARY EVALUATION OF PROTOTYPE This section describes our experiences of implementing the above approach and experimenting with the ePCRN-IIA prototype. The following description focuses on the implementation of the relevant components and services of ePCRN-IIA and does not include all implemented services. The described prototype has been implemented and is being used in three clinical research centres.
Figure 5: Mapping Route (Example)
We found only very minimal changes needed to the domain and data governance ontologies across services deployed at the different clinical centres. While the prototype was implemented only in three different clinical centres, they represented a reasonable sample of the primary care domain in the UK and USA. However, it can be a time consuming task to create domain ontologies for a given domain, this was noted for the first prototype given the lack of knowledge of the interdisciplinary collaboration and evaluation of potential contributing standards. However, this can be improved once these steps are overcome especially for other domains. The mapping between the domain ontology and data sources is a straightforward mechanism and easily configurable.
Figure 4 Domain Concept Capture Interface The architecture of the current prototype has followed, at its core, the general implementation of grid-based applications for the distributed middleware. Although, grid-based services assume more static binding behaviour, which often lack semantic support, they provide an ideal distributed infrastructure. Thus the decision taken was to use a grid infrastructure but modify or add
The data governance ontology was expressed in terms of roles and privileges of access. Roles and Privileges are expressed in relation to the domain ontology concepts opposed to the individual instances of data elements. This enabled dynamically enacting roles in accordance with service data governance policies and
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allowed extensibility not only in defining new roles but also variations in thesame roles across services with no physical corelation to or dependency on individual instances or changes of mappings.
input and valuable contributions to the ePCRN development of this paper.
The meta-registry was implemented using a modified version of the jUDDI version 3.0. It was extended to allow storing semantic meta-data about individual data services beyond the simple service registration data that supports. The meta-data included, in addition to WSDL-type information, descriptions of the content, used codings, available clinical data types in relation to the domain ontology, data changes and service support information. It also included annotations of individual domain ontology mappings and data governance constraints. These are used by the ePCRN-IIA to enable dynamic discovery of and binding to individual data services. Although, it may appear that the current prototype relies on the assumption that meta-data is kept up-todate for successful dynamic binding, our experiments have shown little change tendency in the core semantic elements that may affect the system. In fact, in one case, it was a substantial change in the underlying EHR system itself for which the data service needed to be re-configured, including the mapping ontologies. However, to be exhaustive more experiments are needed to examine the extent of change on its semantic-awareness extensibility. Equally, the architecture may benefit from including standardised semantic service descriptions, such as those based on WSDL-S [2] or OWL-S [8], which need evaluating for the next version.
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9. REFERENCES
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7. DISCUSSION AND CONCLUSION The paper presented the interoperability challenges in eHealth systems and argued that they face similar challenges to those in service-oriented architecture except with focus on data-intensive needs. We assert that using semantic interoperability standards is not sufficiently scalable approach for complex data-intensive closed domains such as eHealth, and that a domain-based semantic driven approach is a more suitable adaptive solution.
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The employed technologies to support clinical research domain presented a number of limitations, including a lack of support for extensible semantic interoperability, flexible security solutions and performance support for large datasets. Semantic interoperability needs to be supported at both system and data level to enable more seamless integration. While semantic web technologies, such as OWL-S, WSDL-S, are addressing some of the issues for system interoperability, in distributed systems these need to be propagated to other parts of the system, service registry (e.g. JUDDI) for example, with a global system view rather than component view. For data semantic interoperability, web 2.0 needs to have inherited easy flexible, opposed to custom-driven, integration of standard terminologies. This may be not as essential for some domains, but critical for other, such as the clinical domain
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The paper outlined an approach to dynamic semantic interoperability based on extensible semantically enriched problem models and domain ontologies. It described briefly the relevant implementation of the approach as part of the ePCRN-IIA prototype and presented some initial results reflecting on its advantages, potential requirements and limitations.
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8. ACKNOWLEDGEMENT
[14] Taweel, A., Rector, A., et al (2006), Grid-based Collaborative Secure Distributed Biomedical Research System, the Universal Computer Science Journal, Vol. 12,
This work has been funded in part by the NIH and NIHR. We would like to thanks all members of the ePCRN project for their
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