=Paper=
{{Paper
|id=None
|storemode=property
|title=EHR4CR: A Semantic Web Based Interoperability Approach for Reusing Electronic Healthcare Records in Protocol Feasibility Studies
|pdfUrl=https://ceur-ws.org/Vol-952/paper_31.pdf
|volume=Vol-952
|dblpUrl=https://dblp.org/rec/conf/swat4ls/HussainOSDJVCD12
}}
==EHR4CR: A Semantic Web Based Interoperability Approach for Reusing Electronic Healthcare Records in Protocol Feasibility Studies==
https://ceur-ws.org/Vol-952/paper_31.pdf
EHR4CR: A semantic web based interoperability
approach for reusing electronic healthcare records in
protocol feasibility studies
Sajjad Hussain1, David Ouagne1, Eric Sadou1, Thierry Dart1, Marie-Christine Jaulent1,
Boris De Vloed2, Dirk Colaert2, Christel Daniel1
1
INSERM, UMR_S 872, Eq. 20, Centre de Recherche des Cordeliers, Paris, France
2
Advanced Clinical Application Group AGFA Healthcare, Belgium
{sajjad.hussain, christel.daniel, david.ouagne, eric.sadou,
thierry.dart, marie-christine.jaulent}@crc.jussieu.fr;
{boris.devloed, dirk.colaert}@agfa.com
Abstract. A major barrier to repurposing routinely collected data for clinical re-
search is the heterogeneity of healthcare information systems. Electronic Health
Record for Clinical Research (EHR4CR) is a European project designed to im-
prove the efficiency of conducting clinical trials. We propose an initial architec-
ture of the EHR4CR Semantic Interoperability Framework using Semantic Web
technologies. We used a model-driven engineering approach to build the seman-
tic resources and derived an HL7-based EHR4CR information model. We plan
to apply existing ontology modularization techniques for obtaining relevant set
of common data elements and clinical terminologies bound to our ERH4CR in-
formation model. EHR4CR platform provides semantic interoperability services
that facilitate the process of defining eligibility criteria based on standard termi-
nologies and then transforming and executing the user-defined SPARQL que-
ries on RDF triplestore representing local EHRs. We plan to evaluate our se-
mantic interoperability framework for patient eligibility determination in the
context of 10 clinical trials running in 11 health institutions.
Keywords: Clinical research, semantic interoperability, electronic health rec-
ord, clinical data warehouses, eligibility criteria, controlled vocabulary
1 Introduction
A major barrier to repurposing clinical data directly from Electronic Health Records
(EHRs) or from Clinical Data Warehouses (CDWs) during clinical trial design and
execution is that information systems in both domains – patient care and clinical re-
search – use different information representations and terminology systems [1]. The
collective efforts of multiple organizations (such as ISO, HL7, CDISC, etc) are cur-
rently focused on defining various standards required to achieve semantic interopera-
bility and bridge the gap between clinical research and patient care by defining map-
pings between their data schemes [2]. However, the current scope of published map-
pings is limited. In the context of patient eligibility, due to the emerging requirements
for representing new eligibility criteria from different domain areas, new mappings
are needed [3]. We argue that integrating patient care and clinical research domains
requires a standard-based and scalable semantic interoperability framework, allowing
dynamic mappings between data structures and semantics of varying data sources.
The EHR4CR (Electronic Health Records for Clinical Research) project aims to
improve the efficiency and reduce the cost of conducting clinical trials, through better
leveraging of routinely collected clinical data in the trial design and execution life-
cycle [4]. In this paper, we propose an initial architecture of the EHR4CR Semantic
Interoperability Framework using Semantic Web technologies. EHR4CR platform
provides semantic interoperability services that facilitate the process of defining eligi-
bility criteria based on standard terminologies and then transforming and executing
the user-defined SPARQL queries on RDF triplestore representing local EHR/CDWs.
2 Related Work: Bridging standards and semantic
interoperability approaches
There have been various attempts in establishing semantic interoperability, develop-
ing different approaches for bridging clinical care and clinical research [5-8]. In this
regard, one of the prominent attempts is the Shared Health Research Information
Network (SHRINE), offering the SHRINE Ontology Mapping Tool (SHRIMP) for
creating association mappings between local and standard vocabularies, and allowing
researchers to define distributed queries over a federated network of i2b2 systems
across multiple institutions [5]. The PONTE project focuses on developing advanced
tools for semantic interoperability between clinical research and EHR data, which
takes into consideration all widely used standards, such as coding systems, terminolo-
gies, vocabularies as well as health messaging standards such as HL7 [6]. The
Linked2Safety project aims, similar objectives being targeted in EHR4CR, to advance
clinical practice and accelerate medical research, by providing pharmaceutical com-
panies, healthcare professionals and patients an innovative semantic interoperability
framework facilitating the efficient and homogenized access to distributed EHRs [7].
A recently launched EURECA project aims to enable seamless, secure, scalable and
consistent linkage of healthcare information residing in EHR systems with infor-
mation in clinical research information systems [8].
Current interoperability efforts in both patient care and clinical research include
defining metadata and vocabulary standards and using these to define Common Data
Elements (CDEs) [9]. The CDISC SHARE project aims at building a metadata reposi-
tory of CDEs based on the BRIDG model and its underlying mapping to the HL7
RIM. Another initiative is the Ontology of Clinical Research (OCRe), which provides
methods for binding external information standards (e.g. BRIDG) and clinical termi-
nologies (e.g. SNOMED CT) [9]. A recently launched initiative, Fast Health Interop-
erability Resources (FHIR) [10], offers the use of RDF-modeled core FHIR resources,
and provides a semantic harmonization framework for establishing semantic interop-
erability between clinical research and patient care domains. To demonstrate the value
of Semantic Web (SW) specifications in bridging patient care and clinical research, a
W3C task on Clinical Observations Interoperability was established [11]. As a use
case for secondary use of EHRs for clinical research, checking clinical trial eligibility
for patient recruitment was chosen and a prototype implementation was achieved. In
line with this approach, we propose EHR4CR semantic interoperability framework
based on SW technologies, where in our case, we define the mappings between stand-
ard clinical terminologies and local EHR terminologies, and use an HL7 based infor-
mation model as a standard interface for querying heterogeneous CDWs.
3 EHR4CR Semantic Interoperability Framework
We propose the EHR4CR Semantic Interoperability Framework for consistent inter-
pretation of clinical data accessed from varying sources (see Figure 1). A template-
based query interface at the User Workbench allows clinical researchers to define
eligibility criteria based on standard terminologies, data elements and value sets using
the EHR4CR Terminology Services. The defined set of eligibility criteria are repre-
sented as SPARQL queries. For querying heterogeneous CDWs through a standard
interface, we adopted the «A_SupportingClinicalStatementUniversal» model, compo-
nent of the StudyDesign, proposed by the HL7 Regulated Clinical Research Infor-
mation Model (RCRIM) Work Group [12]. Structures and Value Sets of Common
Data Elements (CDEs) are defined in order to specify additional constraints on the
high-level EHR4CR information model and to represent the fine-grained clinical in-
formation included in eligibility criteria constructs. Based on the pre-defined termi-
nology mappings in the terminology server, we expand and transform the initial
SPARQL queries based on the local CDW schemas, and execute them across different
CDWs. We transform the query results based on the standardized terminologies, and
then display them at the User Workbench. The key elements are discussed as follows.
Figure 1. EHR4CR Semantic Interoperability Framework for eligibility determination
3.1 EHR4CR Semantic Resources: Extracting modules from standardized
clinical terminologies
Standard clinical terminologies – such as SNOMED CT, LOINC, ICD-10, PathLex,
ATC, etc – contain numerous clinical concepts usually organized in a hierarchy and
interconnected by domain specific relations, provides a comprehensive medical
knowledgebase. However, in particular use-cases and applications only a small frac-
tion of these terminologies are required. Therefore, there have been efforts towards
modularizing and extracting disease-specific ontology modules [13]. In addition, Del
Vescovo et. al. demonstrated a large scale investigation into the modular structure of
state-of-the-art biomedical ontologies, by using their proposed notion of atomic de-
composition, and extracting relevant and “logically complete” fragments from source
ontologies [14]. In order to incorporate the use of standard clinical terminologies in
EHR4CR, we plan to investigate the state-of-the art modularization approaches and
utilize them for extracting modules that represents those terminology fragments that
cover only our domains of interest – i.e. Patient Care and Clinical Research. The ex-
tracted terminology modules will be uploaded into EHR4CR terminology server.
3.2 Building SPARQL Query Endpoint based on heterogeneous CDW Models
For building a SPARQL endpoint, Mate et. al. demonstrated the use of SW standards
in populating i2b2-based CDWs from heterogeneous EHRs [15]. In EHR4CR, we
query patient data from heterogeneous CDWs using SPARQL endpoints that directly
expose the CDW schema as RDF. Immediately formalizing the relational database
(RDB) model of EHR into a CDW represented as RDF triplestore creates the possibil-
ity to use SW technologies – such as defining rules for reasoning and performing
Query Result Transformation and Aggregation. The RDB-to-RDF mapping [16] en-
sures the same semantics between the SPARQL endpoints and the RDB, and in this
case provides a one-to-one mapping, where: (i) a database table is mapped to an
RDFS class (rdfs:Class), (ii) a database table column is mapped to an RDF property
(rdf:Property), and (iii) the database data type of a field is mapped to the XSD data
type range class of the property; with an exception: if a field is a foreign key, its range
is the class that the foreign key points to. Consequently the process of creating an
ontology that describes the CDW model can be automated as discussed in [17].
3.3 EHR4CR Semantic Interoperability Services
In order to establish semantic interoperability between data elements describing eligi-
bility criteria and patient data, we are currently developing the following services.
3.3.1. Terminology Services
For constructing the user-defined eligibility criteria at the Workbench, we build ter-
minology services for selecting preferred medical terminologies and value sets:
Terminology Management Service incorporates extracted modules from standard
clinical terminologies (LOINC, SNOMED CT, ICD-10, HL7 vocabulary, PathLex,
ATC) by loading their schema models through standard protocols.
Terminology Selection Service allows users to select the preferred terminology in
which the user wants to define the eligibility criteria.
Terminology Browsing Service allows users to browse the appropriate terminology
concepts and attached value sets for defining eligibility criteria.
Terminology Mapping Service manages and provides concept mappings between
EHR4CR terminology and local EHR/CDW terminologies.
3.3.2. Query Expansion & Transformation Service
In different clinical terminologies, medical concepts are organized with varied
granularities, therefore while querying based on heterogeneous clinical terminologies,
query expansion becomes a crucial task [18]. In our work, using the EHR4CR
terminology services, we perform query expansion on the user-defined SPARQL
queries by walking through terminology hierarchies for a specific terminology
concept to incorporate its narrower concepts (i.e. sub-concepts) into the query set. By
using the Query Expansion & Transformation Service, we transform the expanded
SPARQL queries based on local CDWs terminology, which can then be executed
across different CDWs to obtain more comprehensive query results.
3.3.3. Result Transformation and Aggregation Service
This service is designed to translate back the query-results obtained from various
CDWs into an integrated result format based on the standardized medical vocabulary
representing the initially given eligibility criteria. It invokes Terminology Mapping
Services to retrieve mappings from local to central terminology codes, and also
performs necessary unit and measurement conversions among query results.
4 Discussion & Conclusion
In this paper, we present EHR4CR semantic interoperability approach for bridging the
clinical care and clinical research domains. In course of developing this framework,
we face several challenges: (i) formally defining patient eligibility criteria including
temporal constraints [19], (ii) dealing with heterogeneity between different EHRs,
(iii) defining mappings between data elements from eligibility criteria and patient
data, and (iv) investigating standard query interfaces for retrieving patient information
from heterogeneous EHRs. We also need to continue our efforts at harmonizing the
EHR4CR Information Model, common data elements and terminology to other stand-
ard-based semantic resources including FHIR, BRIDG, CDISC SHARE (and other
meta data repository initiatives such as caDSR, openMDR, eMERGE) and the Ontol-
ogy of Clinical Research (OCRe) [9].
Our preliminary investigations of 11 hospital EHR systems across Europe suggest
variable quality in the collection of data items most frequently used as eligibility crite-
ria. However, taken together, queries across multiple eligibility criteria are expected
to result in a significant improvement in the accuracy of patient number estimations
than present approaches. We will be conducting these and other evaluations more
formally later in the project, and will report the results in later publications.
Acknowledgements. The research leading to these results has received support from
the Innovative Medicines Initiative Joint Undertaking under grant agreement n° [No
115189], resources of which are composed of financial contribution from the Europe-
an Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’
in kind contribution.
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