Title: Semantic Technology
1Semantic Technology Healthcare in BT
- John Davies
- Head of Next Generation Web BT Research
2Overview
- Introduction to BT and UK Healthcare IT activity
- Relevance of Semantic Technologies to healthcare
IT integration standards (SNOMED-CT HL7) - Implementing SNOMED-CT (large structured
vocabulary) in OWL - Further opportunities and obstacles for adoption
of Semantic Web technologies
3Part 1 BT, Semantic Web and Healthcare
- Next Generation Web Research Group
- Coordinates
- Core partner
- BTs aim is to translate this research into
benefits for customers in divisions such as BT
Global Services
4BT Global Services
- Provides networked IT solutions for multi-site
organisations (gt30 total BT revenue) - The Health division has a prominent role in
10bn UK NHS National Programme for IT - 3bn of contracts for
- Networking
- National application service provider
- Local application service provider for London
5Health IT is heterogeneous distributed
Hospital ward
Community health
Pathology
Pharmacy
General practice
Surgery
Radiology
6Pattern repeated in each health community
7Patient centric Health IT
Patient journey
8NHS Connecting for Health
- National Care Records Service
- A live, interactive patient record service
accessible by health professionals whether they
work in hospital, primary care or community
services - The core of the NHS CRS is provided by the Spine
- Central summary record
- Detailed patient information stays local
- records of detailed history of care,medical
conditions, tests, X-rays scans and other
results. - ? A large distributed system containing
semantically heterogeneous information that must
be integrated to some degree around individual
patient care, with aggregated data sent to the
Spine
9Tools for Healthcare IT Integration
- SNOMED-CT - Consistent use of medical vocabulary
- Systematised Nomenclature Of Medicine - Clinical
Terms - Merger of UK and US efforts with a joint
editorial board - Original development College of American
Pathologists - Now managed by an international organisation
based in Denmark - Large reference terminology (gt400,000 concepts)
- Aim to be machine interpretable for the exchange,
aggregation, analysis of clinical data and to
enable clinical decision support - ? semantic technology!
- HL7 v3 - Consistent messaging between
applications - Specifications for consistent exchange of
information between clinical applications - SNOMED-CT provides a vocabulary whilst HL7
provides the data model with which messages are
built
10Patient centric Health IT
HL7 messages containing SNOMED-CT terms
11What is the Semantic Web?
"The Semantic Web is an extension of the current
web in which information is given well-defined
meaning, better enabling computers and people to
work in cooperation." -- Tim Berners-Lee, James
Hendler, Ora Lassila, The Semantic Web,
Scientific American, May 2001
Today's Web A place where computers do the
presentation (easy) and people do the linking and
interpreting (hard). Why not get computers to
do more of the hard work? XML, RDF, OWL
12Health the Semantic WebSNOMED-CT OWL
- SNOMED-CT
- Concept based
- Clear separation of lexical representation and
conceptualisation - Whipples procedure and pancreatoduodenectomy
terms represent the same medical concept - Cold can mean cold sensation or a common cold
- OWL
- Directly corresponds with OWL Class
- Class(Common_cold)
- RDFS label can be used to represent term labels
(synonyms)
13Pure subsumption hierarchy
- SNOMED-CT
- has an is-a relationship equivalent to logical
implication / subsumption - A patient with asthma implies a patient with a
respiratory disorder
- OWL
- Corresponds to OWL subclass
- SubClassOf(Asthma Respiratory_disorder)
14Property based definitions
- SNOMED-CT
- Relationships with other concepts that partially
or fully define the concept of interest - Description logic reasoner used to classify
concepts
- OWL
- Majority can be regarded as existential
restrictions - Description logic reasoner used to classify
classes
An appendicectomy is defined as a surgical
procedure using the method excision and the site
appendix structure Class(Appendicectomy defined
intersectionOf(Surgical_procedure
restriction(method someValuesFrom Excision
restriction(procedure_site someValuesFrom
Appendix_structure))
15Post coordination
- SNOMED-CT
- Not designed to be complete out of the box
- Extensible at the point of data entry through
post coordination
- OWL
- Corresponds to anonymous class expressions of
OWL-DL
Concepts for kidney excision and left exist,
together with rules that specify it is
appropriate to combine them together. intersectio
nOf(Excision restriction(procedure-site
someValuesFrom intersectionOf(kidney
restriction(laterality someValuesFrom left))))
16Implementing SNOMED-CTSearching for the right
term
- gt400,000 concepts
- Clinician may realistically expect to enter 2-15
terms per encounter (based on Primary care
experience) - Adding terms should not increase documentation
time. - Term search boxes most efficient (for now)
17Key search features
Unordered start of word search within phrase
- Responsive users will not have the time or
willingness to page through multiple pages of
results
18Recording using SNOMED-CTBrowsing for the right
term
- What happens when you cant find the right term?
- Guidance is to use the next most specific term
available and append information as free text - Users need to know specific versus general terms
- Users need to browse the hierarchy
19Browsing features
- Navigating top down in SNOMED-CT not
straightforward (too large)
20Browsing features
- Localised browsing on search hits
21Browsing features
- Localised browsing down on search hits
22Browsing features
- Localised browsing up on search hits
23Terminology reasoning at the point of care
- Description Logic reasoning is required in the
live environment - Why?
- Post coordination (i.e. allowing users to build
anonymous class expressions) - If user has built allergy almond how does the
application work out to retrieve this information
when someone else asks for patients with allergy
to nut?
241 Clinician records clinical data using novel
expression e.g. Allergycaused_byalmond
Clinician A
Healthcare application
25Clinician A
2. Application registers novel expression with
terminology service
Healthcare application
Terminology service
263. Terminology service uses DL reasoner to
calculate novel expressions position in the
subsumption hierarchy of existing concepts
Clinician A
Healthcare application
Terminology service
Description Logic Reasoner
27Clinician A
Healthcare application
Terminology service
Description Logic Reasoner
4. Terminology service provides information to
application on the subsumption hierarchy
including the position of the novel expression
e.g. allergycaused_byalmond is-a nut allergy
28Clinician A
Healthcare application
Terminology service
Description Logic Reasoner
Clinician B
5 Clinician queries for patient data which
contains novel expressions e.g. What patients
have nut allergies?
29Clinician A
Healthcare application
Terminology service
Description Logic Reasoner
6 Application uses hierarchy information provided
by the terminology service to execute query.
(Almond allergies are now known to be nut
allergies).
Clinician B
30Terminology Services example 2
Snomed CT
Data Warehouse
Reasoner
Clinician App
Admin App
Pre-coordination
Post-coordination
Q How many Key hole procedures?
Hernia Repair
Access Instrument?
Laparoscope (implies keyhole procedure)
31Progress in BT
- Proof of concept using Semantic technology
- FaCT Description logic reasoner from University
of Manchester - 30 mins to initially load over 400,000 concepts
in SNOMED-CT - lt10ms to calculate subsumption of simple
anonymous class expressions shown in example - Also evaluating a range of other reasoners
32Other opportunities
- Natural Language Generation
- rendering the formal SNOMED-CT structure into
natural language - Systems interoperability
- Integration of heterogeneous data sources
- Data warehouse mining
- Mapping ontologies (terminology models)
- (Semi-)automatic completion of EPR from
clinicians free text
33NLG - Summarisation
- NLG takes structured data in a knowledge base or
ontology and generates natural language text - Motivation
- natural presentation of ontology fragment for
end-user - automatic documentation of ontologies
- generate textual reports from formal knowledge
- Keeps texts constantly up-to-date reflecting
changes in the ontology - SEKT technology OntoSum (University of
Sheffield)
34Summary Structuring
- Capture regular patterns can be applied
recursively - Describe-Instance -gt Describe-Parents,
- Describe-Attributes, Describe-Part-Whole,
... - Aggregation
- Lexicalisation
- e.g. hasName -gt is called
35Description of HSBC
Financial Institution
Person
Organisation
Bank
lendsTo
lendsTo
HSBC
employees
market-cap
43bn
137000
36Description of HSBC
37Systems Interoperability today
multiple data transformations
38Semantic Technology-based
39Information integration
Applications
Semantic bus
No need to re-engineer legacy data sources
Courtesy Tim Berners-Lee and W3C
40Semantic Web for HealthcareGartner June 2006
- Potential to revolutionize healthcare
interoperability - Enable sharing of knowledge, information and
rules - Ontologies and rules are not new concepts in
healthcare informatics SNOMED-CT - Commoditize decision support currently considered
leading edge in CPR systems - Unify information and decision processes across
admin, clinical, research and strategic boundaries
41Issues in adoption of Semantic technology in
Healthcare
- Ontology management
- Although formalism of SNOMED-CT and OWL very
similar they are not identical - We need to assess the implications and agree with
customers that any differences are acceptable - Todays ontology environments have difficulty
with the scale of SNOMED-CT - Often designed for schema ontologies of 100
classes not 400,000 classes
42Other issues
- RDF (Resource Description Framework) is
promising - a clear mechanism for the identification of
resources (URI Universal Resource Identifier) - a mechanism for the aggregation of data from
distributed sources. - reification which allows statements about
statements. - a link to the well defined semantics of OWL.
- But..
- Novelty exposure to RDF is limited in this
community - Alternative technology Infrastructure and tools
are currently wedded to relational data model - Scalability and performance flexibility of RDF
comes with the downside of reduced performance.
Although examples are appearing of RDF
repositories containing millions of RDF
statements, more evidence of performance and
scalability will be needed to ensure its adoption
(this is improving continually) - Hybrid solutions may allow a compromise
43Is Semantic Technology ready for Healthcare?
- Yes
- certainly in ontology management and reasoning
- a very natural fit
- clinicians are used to ontologies
(terminologies) - No
- Perceived as not quite yet ready for deployment
in other areas such as data management? - Maybe
- Is Healthcare ready for Semantic Technology?
- need Healthcare IT customers to agree
44- Thank you for your attention
- Questions?