TOWARDS INTEROPERABILITY IN e-HEALTH SYSTEMS

A Three-Dimensional Approach based on Standards and Semantics

Jose Manuel Gómez-Pérez

, Sandra Kohler

, Ricardo Melero

, Pablo Serrano

, Leonardo Lezcano

Miguel Angel Sicilia

, Ana Iglesias

, Elena Castro

, Margarita Rubio

and Manuel de Buenaga

iSOCO S.A, Pedro de Valdivia 10, Madrid, Spain

Hospital de Fuenlabrada, Madrid, Spain

Universidad de Alcalá de Henares, Madrid, Spain

Universidad Carlos III, Madrid, Spain

Universidad Europea de Madrid, Madrid, Spain

Keywords: Semantic interoperability in eHealth, CEN 13606, Archetypes, NLP, OWL, SNOMED.

Abstract: The interoperability problem in eHealth can only be addressed by means of combining standards and

technology. However, these alone do not suffice. An appropriate framework that articulates such

combination is required. In this paper, we adopt a three-dimensional (information, concept, and inference)

approach for such framework, based on OWL as formal language for terminological and ontological health

resources, SNOMED CT as lexical backbone for all such resources, and the standard CEN 13606 for

representing EHRs. Based on such framework, we propose a novel form for creating and supporting

networks of clinical terminologies. Additionally, we propose a number of software modules to semantically

process and exploit EHRs, including NLP-based search and inference, which can support medical

applications in heterogeneous and distributed eHealth systems.

1 INTRODUCTION

Healthcare is one of the most information-intensive

sectors of European economies, expected to greatly

profit from research in information and

communication technologies. However, the general

feeling is that, to date, health information

technologies have been mostly the realm of

enthusiasts and the computer wave has not yet

completely arrived.

The European eHealth Action Plan

provides a

mid-term roadmap for improvement of the Health

sector. One of the most challenging issues identified

addresses the interoperability problem between

different e-health systems. Such problem is partially

due to the exponential increase of the number of

medical terminologies (SNOMED CT

, MedDra

ec.europa.eu/information_society/activities/health/policy

www.snomed.org

www.meddramsso.com/MSSOWeb/index.htm

etc.), ontologies (GALEN

, FMA

, etc), and

classifications of diseases and related medical events

and concepts (ICD

, CPT

, etc.) that eventually need

to interoperate with one and another. The same

report highlights the need for standardization as the

key piece to ensure interoperability in this roadmap.

Making eHealth systems interoperable by means of

consensual, standard data formats and protocols will

allow for a significant step forward towards

satisfactory healthcare, accomplishing a number of

goals like improvement of the quality of patient care,

reduction of medical errors, and therefore savings in

terms both of human and economic costs.

Experiences on semi-automated local health systems

have shown a lack of underlying standards for data

exchange, emphasizing as a result that the gap

www.open-galen.com/index.html

sig.biostr.washington.edu/projects/fm/index.html

www.cdc.gov/nchs/icd9.htm

www.ama-assn.org/ama/pub/category/3113.html

205

Gómez-Pérez J., Kohler S., Melero R., Serrano P., Lezcano L., Sicilia M., Iglesias A., Castro E., Rubio M. and De Buenaga M. (2009).

TOWARDS INTEROPERABILITY IN e-HEALTH SYSTEMS - A Three-Dimensional Approach based on Standards and Semantics.

In Proceedings of the International Conference on Health Informatics, pages 205-210

DOI: 10.5220/0001539302050210

 SciTePress

between consumer expectations and actual service

delivery remains unabridged.

The roadmap towards leveraging the

interoperability problem in eHealth has been

favoured by significant advances in information

technologies, especially with respect to knowledge

representation and interoperability. Particularly,

large effort has been invested on the field of

semantic technologies, finally coming of age and

entering the plateau of commercial productivity. The

usage of ontologies

as the main asset of semantic

technologies is currently supported by a wide range

of tools for ontology construction, storing, feeding,

evolution and evaluation like Protégé

or the NeOn

Toolkit

. In addition, mature methodologies and

standards allow for accurate scheduling of ontology

and application developments in terms of effort,

time, quality and finance resources.

As a consequence, the Semantic Web initiative

has proved to offer a reliable solution for large scale

integration and representation problems, which can

significantly contribute to alleviating the

interoperability problem in the Health domain. In

parallel, standardization efforts are going on in

various subdomains for electronic interchange of

clinical, financial, and administrative information

among health care oriented computer systems, for

e.g. definition of communication standards (HL7

)

or information models for electronic health record

(ISO/CEN 13606

norm and openEHR

specifications). The work presented in this paper

combines both approaches (uptake of standards and

semantic technologies) to tackle the interoperability

problem in the Health domain.

Complete information Health systems need to

address three main dimensions (information, concept

system, and inference)

, as well as their associated

resources, which are developed independently. Each

of these components comprises the model itself,

knowledge about a given view of the domain,

metadata, and interfaces to the other components.

Figure 1 shows a version of this vision, instantiated

with the solutions adopted in our approach, based on

OWL

as formal language for terminological and

ontological health resources, SNOMED CT as

lexical backbone for all such resources, and the

en.wikipedia.org/wiki/Ontology_(computer_science)

http://protege.stanford.edu/

www.neon-toolkit.org

www.hl7.org

www.chime.ucl.ac.uk/resources/CEN/EN13606-1/N06-

02_prEN13606-1_20060209.pdf

www.openehr.org

www.semantichealth.org

www.w3.org/TR/owl-ref

standard CEN 13606 (together with the ADL

language for archetype definition). The information

dimension deals with high quality, structured and

timely data collection and representation, allowing

building an information framework for electronic

health records (EHRs). In the present work, we

exploit archetypes as formalism for modelling the

required structures for EHR and defining the context

of the clinical domain where such records belong.

Figure 1: Components of a complete Health system

(source: semantichealth.org).

In our approach, Natural Language Processing

(NLP) support for analyzing patient records is part

of the information dimension and uses the

terminologies provided by the terminology server in

the concept system dimension to identify and

process the information contained in the records. On

the other hand, we use NLP (Jurafsky & Martin,

2000) for extracting data and information from EHR

(free text documents) for further processing. The

NLP of the EHR uses the terminologies provided by

the terminology server to identify and process the

information contained in the records and enable

inferences using the EHR.

The information dimension lies on top of the

concept system dimension, which deals with all the

available terminological and ontological resources

and provides the other two dimensions with uniform

access to such resources. We have addressed the

problem of managing all these resources through the

development of a terminology server, which

consequently allows relating them in a terminology

network.

Finally, the inference dimension exploits both

the concept system and information dimension to

discover new knowledge. Inference and semantic

search through NLP interfaces are some of the most

www.openehr.org/drafts/ADL-12draftF.pdf

HEALTHINF 2009 - International Conference on Health Informatics

206

immediate functionalities on top of which

applications of this vision can be implemented.

The remainder of this paper is structured as

follows: Sections 2 provides an insight to the three

layers of the model, section 3 illustrates the

integrated use of the described infrastructure and

section 4 concludes the paper by describing ongoing

work.

2 THREE LAYER MODEL

2.1 Information Dimension

As introduced above, the information dimension

deals with representing and structuring EHRs. In this

work, we approach this dimension from three

different and complementary perspectives:

1.) The aim of the ISO/CEN 13606 standard is to

normalize the transfer of information between EHRs

systems in an interoperable fashion, without

specifying how to implement them. The reference

model represents the general features of the

components of the health record, how they are

organized and the context information needed to

satisfy both the ethical and legal requirements of the

record. This model defines building blocks for a

formal representation of EHRs. An archetype is the

definition of a hierarchical combination of

components of the reference model, which restrict it

(giving the names, possible types of data, default

values, cardinality, etc.), to model clinical concepts

of the knowledge domain. These structures, although

sufficiently stable, may be modified or substituted

by others as clinical practice evolves.

The Archetype Definition Language (ADL)

allows expressing archetypes. An archetype starts

with a header section followed by a definition

section and an ontology section. The header includes

a unique identifier for the archetype, a code

identifying the clinical concept defined by the

archetype. The definition section contains the

restrictions in a tree-like structure created from the

reference information model. This structure

constrains the cardinality and content of the

information model instances compliant with the

archetype. Codes representing the meanings of

nodes and constraints on text or terms as well as

bindings to terminologies such as SNOMED, are

stated in the ontology section of an archetype.

2.) Processing EHRs requires handling structured,

semi-structured and unstructured data. To process

unstructured data, which is generally free text such

as clinical notes taken by doctors and nursing staff

during patient visits, tools are needed that work

automatically with the language and allow

information to be extracted so it can be easily stored

and consulted. This is especially important in

processes related to Patient Safety.

A series of additional problems exist for working

with reports in free text written by clinical

personnel: heavy use of acronyms and abbreviations;

spelling errors; and including information on people

or organizations, which must be anonymized in

order to comply with laws on health information.

Furthermore, most of the works in this area focus on

the English language, where specific resources in

biomedicine can be found, as MESH, UMLS

(Bodenreider 2004), etc. Nevertheless, in the case of

other languages, such as Spanish, relevant studies

dealing with hospital reports or clinical notes have

not been carried out yet.

Herein we present MOSTAS: A MOrpho-

Semantic Tagger, Anonymizer and Spellchecker for

Biomedical Texts, in order to address these

problems in the information dimension of eHealth

systems. The main objective of MOSTAS is to

analyze clinical reports in Spanish using the

ontological and lexical resources available for the

Spanish language in order to first, pre-process the

clinical reports so that they can be anonymized,

abbreviations and acronyms can be detected and

expanded, medical concepts in the application

domain can be detected. The system’s output is an

XML document with morpho-semantic information

that will facilitate later information retrieval of these

texts.

3.) The remaining module in the information

dimension of our system deals with semantic search

through NLP interfaces using conceptual

knowledge. This module is oriented to implement

main features of document indexing based on the

exploitation of knowledge and ontological resources

included in an integrated way in UMLS, as

SNOMED and MeSH. For the design and evaluation

of the NLP semantic search module, we have

developed a basic system offering interconnection

between health records and a set of scientific

information and health news. Given a query in

submitted by a person, it first retrieves a list of

medical records ordered by relevance in three steps:

i) the query is expanded using concepts included in a

biomedical ontology (i.e.: UMLS); ii) medical

records are ranked using a representation based on

biomedical concepts; iii) then, the user can choose a

record and the system will retrieve several lists of

ranked documents in English: from Pubmed news,

or from article abstracts.

TOWARDS INTEROPERABILITY IN e-HEALTH SYSTEMS - A Three-Dimensional Approach based on Standards and

Semantics

207

2.2 Concept System Model

1.) Addressing the concept system dimension

requires devising a way to deal with the

interoperability problem between the available

terminologies and ontological resources used by the

different computer health systems. In our

framework, we have approached this problem by

developing a terminology server, an open platform

for: 1) Normalizing pre-existing terminologies as

OWL ontologies, 2) Importing available ontological

resources into the server, 3) Relating all these

resources with each other in a terminology network,

where equivalent terms are connected, and 4)

Visualizing and browsing such network. By open,

we mean that the terminology server is fully

extendable with new terminologies, which can be

plugged in as desired. Currently the following

terminologies have been integrated into the system:

ICD-9, official classification of the WHO

for

diseases and health problems, CPC-2

, international

classification for primary care, SNOMED CT, the

most extensive terminology in medical

terminologies, Local Terminologies, containing

terms used by hospital clinic personnel in their

patient records and notes. Using OWL (the W3C

standard ontology language) for representing

normalized medical terminologies is accompanied

by a large number of advantages. In a nutshell, these

can be summarized as: high expressivity, reasoning

capabilities, inference, and a wide tool and

infrastructure support favoured by its status as a

standard and the ongoing contributions from the

knowledge engineering community. Additionally the

number of medical resources available in OWL is

dramatically increasing. As a consequence, in this

work, we have adopted OWL as the reference

language for medical terminology. As part of our

approach, it was necessary to translate legacy

terminologies into OWL in order to incorporate

them into the terminology server. The most relevant

case is SNOMED CT, which required a specific

treatment due to its size and complexity. The

SNOMED CT terminology is distributed across

three text files: one containing the English terms

(>300,000), another for term names in other

languages (Spanish in our version) plus their

corresponding preferred term and synonyms, and a

third one describing the SNOMED CT taxonomy

and the relations between the terms (>1.000,000).

In the translation process, we neither tried to

www.who.int

www.globalfamilydoctor.com/wicc/pagers/english.pdf

improve the overall quality of SNOMED CT

(though several errors were detected (Schulz et al,

2007), (Rector, 2007)) nor did we modify the

concept names. The resulting OWL file contains the

most relevant parts of the terminology without

extending its semantics.

2.) The terminology server follows a three layer

architecture (Figure 2). The lower level stores,

maintains and provides access to the terminologies,

in their OWL forms, currently stored in a Sesame

repository

. The same level allows managing

metadata about the terminologies, like e.g. the

terminology subdomain, the authoring institution, a

short description, etc, which can be useful during

search. The middle layer contains engineering

components that implement three basic

functionalities on top of the lower layer: i) search of

terms both in a single terminology or across several,

ii) mapping related terms of different terminologies,

and iii) term visualization and browsing in and

across terminologies. The higher level of the

architecture contains the GUI components of the

user applications exploiting the functionalities

provided by the underlying layers of the terminology

server. The GUI components can access such

functionalities either programmatically, via a Java

API, or in a loosely coupled way, through web

services.

Figure 2: Terminology server three layer architecture.

3.) Currently, the terminology server provides two

different types of search: The identifier-based

search, where the user types the code of the term in a

before specified terminology and the name-based

search, where the user types the name of the concept

or a part of it. As a shared characteristic, both

searches return the sought term (if any) and situate it

in the terminology network, showing the terms

www.openrdf.org

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related with it in the other terminologies. This search

scenario requires the definition of a terminology

network, where the different terminologies are

connected by means of related terms. For example,

“Acne disorder” in SNOMED CT corresponds to

“Acne” in ICD-9. Currently, in order to support

users in defining the mappings of a term against the

corresponding terms of the remaining terminologies

of the network, we first search its name in those

terminologies. Then, we use SNOMED CT as a

terminology gateway in case no such term name is

detected, i.e. we automatically fetch its synonyms

from SNOMED CT and then search the synonyms

instead. If still no solution is found, the terminology

server tokenizes the term name, discarding stop-

words and starts a new search with these tokens and

their synonyms. Future work includes using

ontology matching

techniques that exploit the

semantics of the terminologies in OWL.

2.3 Inference Model

This section broadly reports on an approach to

convert ADL definitions to OWL and then attach

rules to the semantic version of the archetypes.

Let us consider the following situation. A health

care information system that receives some

OBSERVATION (e.g. “blood pressure”) entry (no

matter where from but fulfilling an archetype

specification) is able to syntactically understand

such information and therefore may deliver it to a

professional, who proceeds with the

OBSERVATION assessment. This is clearly a great

advance for the interoperability of medical systems

but it would be even more interesting if the

observation archetype could tell not only how to

manipulate observation’s values but also how to

assess and evaluate them. Every task that depends on

data analysis and conclusion arrival usually requires

the presence of an expert with enough knowledge to

make a good decision. However if we separate tacit

from explicit knowledge then we could add the latter

in the archetyped concept so the expert only needed

to deal with the former one.

Unfortunately, the ADL language does not

provide support for rules and inference which are

important pieces of clinical knowledge. Besides,

while one of the greatest advantages of two-level

modelling (Beale, 2002) is the carrying out of

archetype definition as a decentralized process, it

allows for contradictory viewpoints to coexist or

even false information to be provided. In addition, a

higher level of normalization of clinical knowledge

could be achieved, encouraging for automated

www.ontologymatching.org

means to reuse knowledge expressed in the form of

rules, which follows the same philosophy of sharing

archetypes.

SWRL

is a W3C recommendation developed to

improve OWL limitations, in terms of inference, by

means of rules. In combination, they add

considerable expressive power to the Semantic Web.

Furthermore, by merging SWRL rules with OWL

ontologies, we will be able to partially automate

decision making process.

Concretely, the complete knowledge workflow,

from archetypes to inference, can be summarised as

follows: 1) Translating ADL to OWL, 2) Mapping

clinical data to OWL instances, 3) Adding SWRL

rules to the ontology, 4) Executing inference.

When translation is finished, the obtained

ontology file should be filled with instances of

concrete clinical data. Depending on the nature of

the data source, an adequate access approach should

be chosen to correctly map each field to individuals’

properties. From our perspective, preferred source

will be the one where supplied XML files are

compatible to the Reference Model syntax. In this

case, instance mapping is a straightforward process.

As a particular implementation, here we adopt an

inference process based on the Jess-Java bridge

provided with the Protégé ontology editor

(Golbreich and Imai, 2004). The Protégé SWRL

Editor is an extension to Protégé-OWL that permits

interactive editing of SWRL rules. It generates OWL

files that include attached SWRL expressions.

The resulting OWL file, enriched with inferred

knowledge, has many possible destinations. For

example it can be directly delivered to the end user

through a compatible interface or stored in a

repository. In the clinical domain, these results

provide means for automatically improving decision

making and monitoring tasks.

3 INTEGRATION

The following example, focused on preventing

pressure ulcers in hospitalized patients illustrates an

integrated, practical use of the three-dimensional

(Information, Concept, and Inference) architecture

described in this paper. Pressure ulcers are a severe

problem for bedridden patients caused by many

different reasons like friction or humidity, which,

not treated in time can become live-threatening. The

goal of the hypothetical system described in this

example is to automatically produce an alarm if a

risk of ulcer is detected for any patient.

www.w3.org/Submission/SWRL

TOWARDS INTEROPERABILITY IN e-HEALTH SYSTEMS - A Three-Dimensional Approach based on Standards and

Semantics

209

First, we define an archetype in the CEN 13606

standard (Information Model) by using the

knowledge of the clinical personnel of this concrete

domain (Inference Model), see Figure 3. The

archetype contains all the information related to the

field of pressure ulcers and defines rules for

identifying possible risks (for example: If the patient

is not able to leave the bed, the risk increases

). The

next step is the linking of the CEN standard with a

reference terminology, in our case SNOMED CT

(Concept System Model).

Now, the risk-detecting protocol can start: The

system is periodically fed with information about the

patients, contained in clinical databases. The terms

contained in such information are processed by the

terminology server, expanded using the mappings

defined across the available terminology network.

This allows the NLP systems to detect occurrences

of the words in the EHRs, which are also corrected if

they had been previously misspelled or abbreviated.

With the information found in the EHRs the rules

built as an extension of the OWL ontologies

corresponding to the ADLs resulting from

implementing the archetype, are immediately

triggered and, if a risk of pressure ulcer is detected

the alarm is triggered.

Additionally, semantic search may offer

information from different EHRs and scientific or

reference documentation related with the particular

case on hand, facilitating decision making to the

clinical personnel.

Figure 3: Example for the interactivity.

4 CONCLUSIONS

AND ONGOING WORK

The interoperability problem in eHealth can only be

addressed by means of combining standards and

http://www.sanitariascaligera.com/index.php?option=com_cont

ent&task=view&id=44&Itemid=69&lang=en

technology. An appropriate framework that

articulates such combination is required. In this

paper, we adopt a three-dimensional (information,

concept, and inference) approach for such

framework. Based on this framework, we have

proposed a novel form of relating the different

terminologies with each other by means of a

terminology server that supports a clinical

terminology network. On top of that, we have also

proposed a number of modules to semantically

process and exploit EHRs, including NLP-based

search and inference, which support applications like

e.g. automatic detection of pressure ulcers.

Nevertheless, all this work is still preliminary,

and we are addressing further tests and evaluation in

real-world systems. Ongoing work lies in this

direction, aiming to demonstrate our approach for

e.g. personal health records. Furthermore, we will

continue the integration of semantic technology in

this framework, especially in the concept dimension,

incorporating novel ontology modularization,

mapping, and context technology in order to

facilitate management of complex and large

terminologies as in the case of SNOMED CT.

ACKNOWLEDGEMENTS

This work has been funded as part of the Spanish

nationally funded projects ISSE (FIT-350300-2007-

75) and CISEP (FIT-350301-2007-18). We also

acknowledge IST-2005-027595 EU project NeOn.

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Archetypes

Domain

Knowledge

Snomed

ICD-9, ICPC-2

etc.

Clinical personnel

develops

Archeype

In CEN-

standard

Linking CEN with

terminologies

EHR

repository

Concept System

Model

Inference Model

Information

Model

Term.

Server

ALARM

Applying

rules of

archetype

START

OWL

NLP

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