Medical Semantics, Ontologies, Open Solutions and EHR Systems

mumpsimuspreviousAI and Robotics

Oct 25, 2013 (3 years and 8 months ago)

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1


Published in Virtual Medical Worlds


June 2009

http://www.hoise.com/vmw/09/articles/vmw/LV
-
VM
-
09
-
09
-
6.html



Medical Semantics, Ontologies,

Open Solutions and EHR Syst
ems


Introduction


This article offers a high level introduction for management to the topics of medical
s
emantics
and

ontologies

as they relate to healthcare and health IT systems.
Medical semantics and ontologies tend to
be poorly understood by many audi
ences and
are subjects that
sound more complex and difficult to
grasp than they really are. In this article the authors attempt to

briefly

introduce and try to
demystify
these topics
,

and
hope
fully,

to
generate some enthusiasm

among skeptics
.
The art
icle a
lso explores some
of the 'open s
ource
'

tools that have emerged related to this area, and how all this
ties in
to the future of
Electr
onic Health Records (
EHR) an
d Health Information Exchange (
HIE) systems.


Medical Semantics


Semantics is the study of meani
ng in communication. It is also the study of interpretation of signs as
used by agents or communities within particular circumstances and contexts.
Did anyone read or see
"The Da Vinci Code"?

A simple example involves the meaning of the
word
'
cold
'.


It
h
as completely
different meanings in the phrases “I am cold” v
ersus

“I have a cold.”

W
ord
s

may have

different
meanings depending on the context
in which

the word is used.

See
http://www.state
master.com/encyclopedia/Semantics


The application of semantic technology to the medical domain
will
provide IT systems with the ability
to better understand terms and concepts as data is transmitted from one system to another, while
preserving the meani
ng of the content.
For t
his

process to work effectively, extensive investments are
being made involving

the classification of
medical
terms and their meanings. Tools in this area make use
of classification systems that produce controlled vocabularies, lexi
cons, taxonomies and ontologies.


For humans, the meaning of a given word is normally obtained by consulting a dictionary or by looking
at the context where the word is being used. The computer does not make use of

textual dictionary
definitions

and has n
o pre
-
existing repository of contexts
,

but instead requires a semantic representation
that is simpler and more precise. Natural language processing systems represent the meaning of a given
word or phrase using a symbol or code. For an Electronic Medical Re
cord (EMR) system, “heart” and
“cardiac” are two unrelated terms. For hu
mans, however, both terms have the same '
semantic
'

meaning.


Increasingly, healthcare institutions have access to computeri
zed patient
medical
records containing
massive amounts of raw

data. Much of the available data are in textual form as a result of transcription
of dictated reports, use of speech recognition technology, and direct entry by health care providers.
While textual data are convenient for tasks such as review by clinician
s, they present significant
obstacles for graphic presentation, searching, summarization, and statistical analysis. The techniques of
natural language processing translate the meanings of terms in the record into more meaningful use.


At this point in tryi
ng to provide a simple explanation, you're either
'
nodding
'

your head in agreement, or
'
nodding off
'

in sheer boredom.
T
o briefly summarize
, m
edical semantics is a mechanism for applying
2


tools and techniques that leverage semantic knowledge to enhance the
use and utility of healthcare IT
systems.


Semantic Interoperability


IEEE defines ‘Interoperability’ as the ability of two or more systems or components to exchange
information and to use the information that has been exchanged. (
http://www.ieee.org/portal/site
)


'
Semantic interoperability
'

is defined by the National Alliance for Health Information Technology
(NAHIT) as “the ability of different information technology systems, software applications and
networ
ks to communicate and exchange data accurately, effectively and consistently so providers can
use the information as they care for patients
.” (
http://www.nahit.org
)


It is important to emphasize that there are levels of

interoperability, sort of a “Pyramid of Health Data
Interoperability” if you will
,

that help
s

facilitate the enhancing functions of semantic interoperability and
ontologies.
What follows is
a high
-
level description of the
International Standards Organizat
ion (
ISO
)

Open Systems Interconnection (OSI)
model. Envision that this “pyramid of health interoperability” has
the following layers:


ISO OSI Seven Layer Model

7.)


Application

Layer


6.)


Presentatio
n Layer



5.)


Session

Lay
er


4.)


Transport

Layer


3.)


Network

Layer


2.)


Data Link

Layer


1.)


Physical

Layer



The ISO OSI Seven Layer Model for Networ
king

See
http://mike.passwall.com/networking/netmodels/isoosi7layermodel.html



One of the most
important

ways s
emantic interoperability services and resources in healthca
re can be
used relates to reconciling clinical data contained in
diverse
EHR systems.
Semantic interoperability is a
concept that will definitely contribute to improvement in health care over time because it
will
deliver the
right meaning of medical termin
ology to each collaborat
ing system user

every time,
via

a service
-
oriented web
-
based solution.


Semantic Web


The
Semantic Web

provides a common framework that allows
data

to be shared and re
-
used across
application, enterprise, and community
boundaries.

T
he

Semantic Web is an evolving extension of the
World Wide Web (WWW) in which web content can be expressed not only in natural language, but also
in a format that can be read and used by software agents, thus permitting them to find, share and
integrate in
formation more easily.
See
http://www.w3.org/2001/sw/



At its core, the Semantic Web comprises a philosophy, a set of design principles, collaborative working
groups, and a variety of enabling technologies. Some

elements of the semantic web are expressed in
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formal specifications such as the Resource Description Framework (RDF), a variety of data interchange
formats (e.g.
RDF/XML
,
N3
,
Turtle
), and notations such as RDF Schema and the Ontology Web
Language (OWL), all of which are used to provide a formal description of concept
s, terms, and
relationships within a given knowledge domain


Ontologies


Ontology is a data model that represents a set of concepts within a domain and the relationships between
those concepts. It is used to reason about the objects within that domain. Ont
ologies are used in artificial
intelligence, the semantic web, software engineering, biomedical informatics, and information
architecture as a form of knowledge representation about the world or some part of it. Ontologies
generally describe:




Individuals:

the basic or primary objects



Classes:

sets, collections, or types of objects



Attributes:

properties, features, characteristics, or parameters that objects can have and share



Relations:

ways that objects can be related to one another



Events:

the changing o
f attributes or relations


An
ontology language

that may be used in an example like this, is a formal language used to encode the
ontology. There are a number of such languages for ontologies, both proprietary and standards
-
based.

For example:




OWL



Ontology Web Language (OWL)
is a family of knowledge representation languages for
authoring ontologies, and is endorsed by the World Wide Web Consortium.




KIF

-

Knowledge Interchange Format (KIF) is a computer
-
oriented language for the interchange of
knowledge among disparate computer programs.




Cyc

is an
artificial intelligence project

that attempt
s to assemble a comprehensive ontology and
database of everyday common sense knowledge, with the goal of enabling AI applications to
perform human
-
like reasoning. It
has its own ontology language called
CycL
.



RIF

-

Rule Interchange Format (RIF)
effort involves the development of a format for interchange of
rules in rule
-
based systems on the semantic web. The goal is to create an interchan
ge format for
different rule la
nguages and inference engines.



‘Open Source’ Semantic/Ontology Solutions


Open source describes a broad general type of
software license

that makes source code available to all
with relaxed or non
-
existent
copyright

restri
ctions.
It is an explicit 'feature' of open source that it put
little or no restrictions on the use or distribution of the code by any organization or user.
There are many
open source projects and tools available related to semantics and ontologies that ca
n be found at

www.sourceforge.net

, such as:


OntoWiki and Powl

-

OntoWiki is a semantic collaboration platform for the development of Semantic
Web knowledge
bases. Powl is
a
web
-
based ontology authoring and management solution for the
Semantic Web.

Nepomuk Semantic Desktop Project

-

NEPOMUK brings together researchers, industrial software
developer
s, and users in a collaborative open source project to build the Social Semantic Desktop
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solution.

CuiTools

-

A Perl package for supervised word sense disambiguation (WSD) experiments that utilize
f
eatures extracted from the Unified Medical Language System (UMLS). The word Cui comes from the
Concept Unique Identifiers in

the UMLS.


Many other related open source projects are underway such as:




Kowari



RDFLib



Jena



Protégé



SWOOP



CO
-
ODE



Semant
ic/Ontology Projects & Tools in Healthcare


The following are examples of open source
Medical

Semantics/Ontology projects and tools.


ARTEMIS Project

-

A Semantic Web Service
-
based P2P Infrast
ructure for the Interoperability of
Medical Information Systems.IST
-
1
-
002103
-
STP In early stages of development.

MII Medical NLP Toolkit

-

This is a toolkit for medical natural language proce
ssing (NLP). The core
engine is general enough to be used in a variety of text processing domains, though the toolkit includes
specific support for medical reports

and patient de
-
identification.

ONTO
Derm

-

ONTODerm is a specialty specific ontology for dermatology to integrate dermatology
with medical software systems.
In early stages of development.

Medical Language Processing

-

Natural languag
e processing of free
-
text clinical documents into an
information representation in XML accessible via a rich system of categories familiar to clinicians.
In
early stages of development.

Other major
health care related
projects, tools and organizations of i
nterest include:




GALEN




Gene Ontology




UMLS




medSLT




SNOMED
-
CT




SAPPHIRE


*
Also take the time to visit the
Semantic Web Tools wiki at
http://esw.w3.org/topic/SemanticWebTools




Some examples o
f published ontologies include:



Protein Ontology



WordNet Semantic Lexicon



Foundational Model of Anatomy (FMA Ontology




Systems Biology Ontology (SBO)



General Ontology for Linguistic Description (GOLD)




Gene Ontology



OntoSelect

monitors the

web to provide an access point for ontologies on any possible topic or domain
that is automatically updated, organized in a meaningful way and with support for ontology search and
selection.
Swoogle

is another good

semantic/ontology web search engine that is available for use. Also
consider
try
ing

Ontaria

which provides a searchable and browsable
directory of semantic web data.

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Semantic Interoperability, Ontologies and Electronic Health Record (EHR) Systems


Medical information syst
ems need to be able to communicate complex medical concepts
unambiguously, even those expressed in different languages. This is obviously a difficult task and
requires extensive analysis of the structure and the concepts of medical terminologies. It can be

achieved
by constructing medical domain ontologies for representing medical terminology systems. This not a
trivial task.


An information model is needed to describe the relationships of different data elements in a patient’s
medical record. Data element
s and relationships in the information model are often tacitly assumed.
Difficulty arises in the situation where two disparate EHR systems make different assumptions. This
leads to the need for an
Information Mediation Service (IMS) to perform application
-
to
-
application
mappings between EHR systems. This is not a trivial matter. Work on medical domain ontologies and
information models have already been in progress for almost two decades.


VA VistA
Electronic

Health

Record (E
H
R) System
-

Lexicon Utility

The adoption of a standardized reference for clinical terminology across Veterans
Health Administration

(VHA) facilities enables clinical information to be recorded, transmitted, retrieved, and analyzed in a precise
manner independent
of clinic or medical center. The scope of the Lexicon Utility, used within the VistA
Computerized Patient Record System (CPRS), is to express diagnostic clinical problems in easy
-
to
-
understand terminology and associate these terms to coding systems such as

ICD, DSM, NANDA, etc.

It
works in conjunction with
other
VistA applications such as
the
Problem List, Encounter Form, and Text
Integration Utility (TIU) and provides a comprehensive API so that any application that needs to use
standardized terminology c
an be interfaced. Major features or functionality include:



Provides a basis for a common language of terminology so that all members of a health care team can
communicate with each other.



Provides terminology that is well defined, understandable, unique i
n concept, and encoded by multiple
coding schemes.



Provides for site modification of text presentation, term definitions, synonyms, shortcuts, and keywords.



Provides the ability to upgrade coding systems (e.g., ICD
-
9
-
CM to ICD
-
10) and to add, change, and

delete codes.



Provides for limited views of vocabulary (lexicon subsets).



Allows each site to add its own vocabulary to the lexicon.



Accepts the provider terminology if a search of the dictionary does not find a match.



Uses subsets of terms based on s
pecialty or clinic.



Allows abbreviations or shortcuts to provide quick access to frequently used definitions.



Supp
orts CPT terminology and codes.

See
http://www.va.gov/vista
_monograph/docs/vista_monograph2005_06.pdf



Social Security Administration (SSA)
Healt
h IT Semantic Interoperability Pilot Project


The Health IT Semantic Interoperability pilot was developed in by the SSA as a proposed proof
-
of
-
concept in
2006 for integration of a Health IT and Disability Determination business process. In particular, this

business
process requires data sharing and processing across various governmental and private sector enterprises such as
SSA, VA, CMS, HHS, NARA, hospitals, healthcare providers, insurance providers, legal communities and others.

See
http://colab.cim3.net/file/work/hit/hitop/SSA%20HIT%20Semantic%20Pilot.doc

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In addition to the

'open source' or 'public domain'
VA VistA system, there are
other
examples of projects
aimed at

demonstrat
ing

the applicability of semantic web and ontologies with EHRs.

For example:

The
Art
emis Project and the
Artemis Message Exchange Framework

(AMEF
)

ha
ve

been

developed
to provide the exchange of meaningful clinical information among healthcare institutes through semantic
mediation. Some of the achievements of the Artemis project
include
:



Finding and retrieving clinical information about a particular patient from different healthcare
organizations where concrete sources are unknown.



Demonstration of a very robust, but highly flexible approach to security and privacy.



The
partnering entiti
es

of the Artemis project developed Web services for exposing their existing
healthcare applications and patient data.

See:
http://www.srdc.metu.edu.tr/webpage/projects/artemis/


The Tel
emedicine and Advanced Technology Research Center (TATRC)

within the U.S. Department of
Defense (DoD)
has initiated the
TATRC Natural Language Processing (NLP) Systems Project
.

The
purpose of this project is to design and develop a natural language process
ing engine that is compatible
with Armed Forced Health Longitudinal Technology Application(AHLTA) and is linked to MEDCIN,
UMLS and SNO
-
MED CT ontologies. The goal is to be able to process semi
-
structured or free text note
sections of AHLTA and be able to
capture both contextual and structured terms for surveillance and data
mining. The tool must show how these captured structured terms can be extracted and searched from the
clinical data repository. The task is to design and develop a natural language proc
essing engine which
can be used to allow providers to document their care in the electronic health record in a natural way,
without forcing them to use structured documentation. Currently, much of the documentation is "too
structured", forcing providers to

use a very hierarchical structure of MEDCIN. There is significant
evidence that this method causes significant errors and the result is a documented note which does not
accurately capture the essence of the patient encounter
. See
http://www.ehealthdesigns.com/?p=237



Semantic Interoperability and Privacy & Security


At the Health IT Definitions Project Public Forum held on January 16, 2008, Dr. Karen Bell, former
Director of the Office of Health IT Ado
ption within the Office of the National Coordinator for Health IT
(ONCHIT), said there were two things they wished they had done sooner: vocabulary

harmonization and
privacy & security. This gave rise to the establishment of the Health Information Technolo
gy Ontology
Project (HITOP) working group by ONCHIT.
See
http://semanticommunity.wik.is/Federal_Semantic_Interoperability_Community_of_Practice



A Seman
tic Web Information Infrastructure must comply with commonly accepted privacy and security
policies and standards related to handling sensitive patient data contained in electronic medical records
(EMR).
For example, the SAPHIRE Project in Great Britain e
mploys comprehensive privacy and
security mechanisms to complement their infrastructure, which is based on end
-
to
-
end and system
-
to
-
systems connections with semantic interoperability. Specifically, EU directives 95/46/EC and
2002/58/EC presenting the gener
al principles of processing of personal data were taken into account,
with particular attention paid to recommendation R(97)5 of the Council of Europe discussing protection
of medical data collected and processed automatically.
See
http://kb.healthgrid.org/record/71

Work by the Center for Clinical Translation Sciences (CCTS) at the University of Texas Health Science
Center at Houston may also be of some interest. The CCTS Environment, Documentation, and
Aut
horization models enable the system to dynamically and automatically contextualize availability,
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access, utilization, and retrieval of all informational resources governed by the CCTS program and its
collaborators through combinations of constraint based o
n role, investigator, research project or research
question. Thus, CCTS utilizes Semantic Web technologies not only for integrating, repurposing and
classification of multi
-
source clinical data, but also to construct a distributed environment for
informat
ion sharing, and collaboration online with security and privacy of personal data.
See
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2646248


Benefits of Using Ont
ologies & Semantic Interoperability Systems


The following are some ways the healthcare system can be improved by using medical ontologies and
semantic interoperability tools and practices include:




Improve accuracy of diagnoses by providing real time corr
elations of
symptoms, test results and
individual medical histories through standards
-
based systems for
s
ystematic cross
-
checking
diagnoses;



Increase prompt payment of Medicare and Medicaid claims by
r
educing billing questions through
adoption of IT standa
rds for clinical care codes, medical nomenclature, lab tests, etc.,



Reduce the burden of fraud on the overall system by enhancing the capability to detect fraud by the
use of semantic interoperability tools.



Ontologies can help build more powerful and more

interoperable information systems in healthcare.



Ontologies can support the need of the healthcare process to transmit, re
-
use and share patient data.



Ontologies can also provide semantic
-
based criteria to support different statistical aggregations for
di
fferent purposes.



Possibly the most significant benefit that ontologies may bring to healthcare systems is their ability
to support the indispensible integration of knowledge and data.


Conclusions


The set of technologies associated with semantics and ont
ologies in health care are, relatively speaking,
still in their infancy or early childhood. While there are high expectations, only modest progress has
occurred to date.


Partnerships between major technology vendors such as commercial database companie
s and large scale
integrators working in collaboration on public
-
private sector EHR projects will help break through some
of the existing major barriers.


With the ease of posting structured lists on the Internet, and with Extended Markup Language (XML)
as
an emerging standard for such lists, it is likely that the next decade will witness an explosion of medical
ontologies available in the public domain.


Next Steps

The following are some recommendations and next steps healthcare organizations should cons
ider
taking with regards to Ontologies and Semantic Interoperability solutions.




Consider establishing a workgroup to identify functional requirements and/or potential uses of
medical o
ntologies and semantic interoperability
systems for use by your healthc
are organization.

8




Conduct a detailed literature search and market survey annually and obtain lessons learned from
medical o
ntologies and semantic interoperability
projects underway at other institutions.



Identify potential organizations to collaborate wi
th on the research, development, testing and use of
medical o
ntologi
es and semantic interoperability
, e.g. medical schools, vendors.



Investigate changes in clinical and IT practices that may need to be made in anticipation of utilizing
medical o
ntologies
and semantic interoperability systems
.



Initiate and fund a pilot project(s) and complete a detailed cost benefit analysis of investments in this
arena. The pilot may involve use of either a commercial or open source solution.




Selected Reference Web Si
tes




NLM Lister Hill National Center for Biomedical Communications
-

http://lhncbc.nlm.nih.gov/lhc/servlet/Turbine/template/research,langproc,Medical
Ontology.vm




EU eHealth Ontology Project
-

http://www.ehealthserver.com/ontology/




Saarland University INFOMIS
-

http://www.ifomis.uni
-
saarland.de/




Semantic Interoperability Community of Practice
-

http://semanticommunity.wik.is/Federal_Semantic_Interoperability_Community_of_Practice




The National
Center for Biomedical Ontology
-

http://www.bioontology.org/about.html




International Semantic Web Science Association
-

http://www.iswsa.org/




The Semantic Web In

Action
-

http://www.scientificamerican.com/article.cfm?id=the
-
semantic
-
web
-
in
-
action






Authors:


Peter J. Groen

is
an adjunct faculty member
of the Computer &
Information Science Department at Shepherd
University in West Virginia
. He

is one of the founders of the Shepherd University Research Corporation

(SURC)
-

see
www.shepherd.edu/surc/cosi




Marc Wine

works is a s
enior health systems advisor with Northrop Grumman Information Solutions, served as
senior advisor to the U.S. Department of Defense, Telemedicine & Advanced T
echnology Research
Center(TATRC). He also served with the Veterans Health Administration (VHA)

fo
r most of his career
. He

is
also
an adjunct faculty
member
at

The George Washington University

where he teaches Health IT Systems
Management. You can contact him at
winemash@aol.com.