The Pain Points in Health Care and the Semantic
Web
Advanced Clinical Application Research Group
Dr. Dirk Colaert MD
Healthcare is changing…
Today
Scope
Cure Patients
Focus
On the process and provider
Time
Symptomatic, curative
Tomorrow
Care for Citizens
On the patient
Preventive, lifetime
Location
Hospital
Decentralized, at home
Methods
Invasive
Less invasive
De processes are changing …
Today
Clinical
Decisions
Personal preferences
The Process
Fragmented, isolated
Experience
Order Process
Information
Individual
Manual
Fragmented, isolated
Tomorrow
Guide lines / evidence based
disease mgt.
Best Practices
Automated
Consolidated / complete
IT is changing …
Today
Tomorrow
Technology
Isolated systems
Integrated systems
Data access
Limited, Difficult
Any time, any place
Data integrity
Manual/error prone
Data
completeness
Fragmented
Data availability
Slow
Systematic mgt. and control
Consolidated
Real time
The health care is under pressure ...
• Costs must decrease
• Quality must increase
– E.g. Medication errors: in
the US 80.000 people
died in 2004. (=8th cause
of death)
The Hospital
High Quality
Cost Effective
Medical Knowledge
Activities
needs
Assessment
produces
needs
Information
Healthcare as a Process
objective
Society
Assesment
subjective
operational
Planning
Diagnostic Action Therapeutic Action
Input
Process
Medical
Care Action
Community
Output
Healthcare as a Process: pain points
Isolated information
Complex desicions
Fragmented information
Lack of training
Not accessable information
Changing knowledge
Too much information
Medical errors
Bad information presentation
Inefficient workflow
Only clinical data is kept (no knowledge)
Understaffing
Some information is not computer usable (free text, image
features, (genome in the future))
No feed back to medical community and society
No operational information
No infrastructure information
No common language
Input - Output
Process
Clinical Desicions
Information
Medical
Community
operational
Society
objective
subjective
Assesment
Planning
Workflow
Action
Cure for the pain points – wave 1
PAS: Patient Adminstration System
HIS: Hospital Information System
Result Distribution
Input - Output
Process
Clinical Desicions
Information
Medical
Community
operational
Society
objective
subjective
Assesment
Planning
Workflow
Action
Cure for the pain points – wave 2
PACS: Picture Archiving And Communication Sytem
PAS: Patient Adminstration System
HIS: Hospital Information System
CIS: Clinical Information System
Care
Order Entry
Medication prescription
Result Distribution
Input - Output
Process
Clinical Desicions
Information
Medical
Community
operational
Society
objective
subjective
Assesment
Planning
Workflow
Action
Cure for the pain points – wave 3
feature extraction from unstructured or massive
information (images, free text)
Information filtering
Decision support
Advanced connectivity
Semantic driven UI
Content
Clinical Pathways
Workflow optimization
Evidence based medicine
Intelligent patient portals
Clinical Trials (in- and exclusion criteria, data mining)
Terminology
Remote data capture
Common to all Community
this isHealthCare
…
Input - Output
Process
Clinical Desicions
Information
Medical
Community
operational
Society
objective
subjective
Assesment
Planning
Workflow
Action
Connected Knowledge
• Knowledge is a higher form of Information
• Knowledge (meaning, understanding) begins when facts and concepts
(information) are connected
• Latin ‘intellectus’ comes from intelligere, inter + ligere = connect between
• A formal description of a domain, using connected facts and concepts is called
‘an ontology’
• The W3C organization provides standards: RDF (Resource Definition
Framework) , OWL (Ontology Web Language)
• The “semantic web”: use the W3C standards and the inherent communication
and linking properties of the WWW.
• By linking ontologies they can be merged to “connected knowledge”: very
powerfull but dangerous!
Simple ontology
hobbies Religion
Audi
Salary
Opel
Other Brands
Me
Model of
Instance of
owns
A3
A4
ABC 1234_567
Audi
A6
has color
Green
Knowledge: traditionally ‘assumed’
visit
Aspirin
?
Lab Test
Tenormin
hypertension
Connected Knowledge: explicit
visit
Conclusion of
Aspirin
Lab Test
Tenormin
Indication for
hypertension
threated by
Connected Knowledge: scalable
Connected Knowledge
Examples of ontologies and rules: medical vocabulary, patient clinical data,
infrastructural data
Because ontologies are formaly described, computers can use them, take
rules and reason about the concepts.
Technologies, able to connect facts into ontologies, connect ontologies to each
other and reason about it with rules gives us the means to improve vastly the
current painfull processes in healthcare.
Examples:
Use of a Terminology Server for Controled Medical Vocabulary
Decision support and clinical pathways
Terminology Server
•
Purpose:
– Easy entry of data into the medical record keeping ‘freedom of speech’ and still
be able to document in a uniquely defined and coded way. (e.g. ICD9)
•
Example
– Data entry: “blindedarm onsteking” (Dutch)
– Results in: ICD9 XYZ (“appendicitis”)
– No single part of the search string is found in the result. This can only be
achieved by a system ‘knowing’ the domain.
Concept
inflamation of
Concept
Appendicitis
Appendix
Term for
Code
XYZ
ICD9 code for
Term for
Term
Term
Appendix
Blindedarm
Decision Support and Clinical Pathways
• Clinical Pathway: a way of treating a patient with a
standardized procedure in order to enhance the efficiency,
increase the quality and lower the costs.
• Usually represented in a script book and/or flow chart
diagram
• Issues with conventional Clinical Pathways:
– Not very dynamic: “one size fits all”
• Not adapted 100% to the individual patient
– Not mergeable
• How can you enroll a patient into 2 pathways?
– Difficult to maintain: mix op procedural and declarative knowledge
Agfa’s Advanced Clinical Workflow research
• Combining
– knowledge, declared in rules and concepts (the ontologies)
• Medical domain
• Clinical data about the patient
• Operational (local policies)
• Infrastructural (machines, people)
• Workflow theory and ontology (pi-calculus)
• Fuzzy sets theory and ontology
• Calculating the procedure to follow: the next
step(s)
• After each action a recalculation is done
Adaptable Clinical Workflow Framework
Assesment
Planning
Diagnostic Action
Therapeutic Action
Care Action
Medical
Community
Society
subjective
objective
operational
Adaptable Clinical Workflow (compare to
GPS)
Adaptable Clinical Workflow (compare to
GPS)
After deviation from
the calculated course
the system adapts the
itinerary
From pixel to community
The box is a fractal unit that
can be scaled from “pixel to
community”
Guidelines
Human Interaction
Policies
Clinical Data
Events
Requests
Recommendation
(Local,
Operational,
Community,
...)
Desicion support
Desicion
Action
Country  World  Healthcare Management
Region  Disease Management
Institution  Clinical Pathway
Department  Order
Workstation/User  Task
Application  Event
communication and event bus: share knowledge and evidence
Country  World  Healthcare Management
Region  Disease Management
health monitoring
process
Institution  Clinical Pathway
clinical decision
process
Department  Order
scheduling
process
workflow
monitoring process
Workstation/User  Task
task process
work list process
Application  Event
form generator
Issues when merging ontologies
• Inconsistencies
– Ontologies are build without other ontologies in mind. When merged they
can contain contradictions.
– This can be detected and brought to the attention of the user.
• Semantic differences
– See the example avove about “Audi” as a car and “Audi” as a brand.
– Can be solved by using standard ontologies as much as possible (e.g.
SNOMED in the medical domain)
• Side effects
– Duplicate examinations
– Bad sequence
– Wrong conclusions
• Trust
– When an external ontology is about to be merged the source must be
trustworthy
Duplicate examinations
• CP 1
–
–
–
–
Day 1 CP1_Action1
Day 2 Lab test: RBC
Day 3 CP1_Action3
Day 4 CP1_Action4
• CP 2
–
–
–
–
Day 1 CP2_Action1
Day 2 CP2_Action2
Day 3 Lab test: RBC
Day 4 CP2_Action4
• CP 1+2
– Day 1
• CP1_Action1
• CP2_Action1
– Day 2
• Lab test: RBC
• CP2_Action2
– Day 3
• CP1_Action3
• Lab test: RBC
– Day 4
• CP1_Action4
• CP2_Action4
Solution
• By adding extra rules this can be solved.
• “If the outcome of an examination is valid for x
days than any duplicate examination within that
period can be canceled”
• These are “rules about rules” or “policies”
Bad sequences
• CP 1
–
–
–
–
Day 1 CP1_Action1
Day 2 RX+contrast
Day 3 CP1_Action3
Day 4 CP1_Action4
• CP 2
–
–
–
–
Day 1 CP2_Action1
Day 2 CP2_Action2
Day 3 RX
Day 4 CP2_Action4
• CP 1+2
– Day 1
• CP1_Action1
• CP2_Action1
– Day 2
• RX+contrast
• CP2_Action2
– Day 3
• CP1_Action3
• RX
– Day 4
• CP1_Action4
• CP2_Action4
solution
• Extra rule
– “Examination X cannot be performed within x days after the
administration of contrast medium Y”
• Policy
– Rules can be abstracted further into policies:
– “All examinations must be checked against exclusion criteria”
Wrong conclusion
• CP Rheuma
– Rule x
– Rule: If pain 
Aspirine
– Rule y
• CP Gastric Ulcus
– Rule a
– Rule b
– Rule …
• CP Rheuma+GU
– Rule x
– Rule: If pain 
Aspirine
– Rule y
– Rule a
– Rule b
– Rule …
Wrong conclusions
• Because of the specific focus when making a
clinical pathway, merging CP’s can potentially
be dangerous.
• Solution:
– Detect possible patterns and add policies to cope with them.
– For example: “For any medication prescription (outside the
scope of the original CP), check interaction with the medical
history and problems of the patient”
Trust
• Inference engines can produce, as a side
product, the proof that, what is concluded, is
logically true.
• We need standards to communicate and
represent these proofs
Conclusion
Ontologies, together with theories (rules) can help health care
providers to treat patients with better quality and less costs.
The intrinsic possibility of connecting ontologies and theories
allow systems and people to use each others experience.
Extra policies can possibly detect and neutralize problem
patterns within merged ontologies. Further research is needed
here.
Scaling ontologies and theories outside the boundaries of the
hospitals can be used to orchestrate effective community
healthcare and regional healthcare programs.
Thanks