The Pain Points in Health Care and the Semantic Web

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The Pain Points in Health Care and the Semantic
Web

• Advanced Clinical Application Research Group
• Dr. Dirk Colaert MD

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Healthcare is changing
Today
Tomorrow
Scope
Cure Patients
Care for Citizens
Focus
On the process and provider
On the patient
Time
Symptomatic, curative
Preventive, lifetime
Location
Hospital
Decentralized, at home
Methods
Invasive
Less invasive
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De processes are changing
Today
Tomorrow
Clinical Decisions
Personal preferences
Guide lines / evidence based
The Process
disease mgt.
Fragmented, isolated
Experience
Individual
Best Practices
Order Process
Manual
Automated
Fragmented, isolated
Consolidated / complete
Information
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IT is changing
Today
Tomorrow
Technology
Isolated systems
Integrated systems
Data access
Limited, Difficult
Any time, any place
Data integrity
Systematic mgt. and control
Manual/error prone
Fragmented
Consolidated
Data completeness
Data availability
Slow
Real time
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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)

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The Hospital
High Quality Cost Effective
Medical Knowledge
needs
needs
produces
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Healthcare as a Process
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Healthcare as a Process pain points

• Complex desicions
• Lack of training
• Changing knowledge
• Medical errors
• Inefficient workflow
• Understaffing
• No operational information
• No infrastructure information
• No common language

• Isolated information
• Fragmented information
• Not accessable information
• Too much information
• Bad information presentation
• Only clinical data is kept (no knowledge)
• Some information is not computer usable (free
  text, image features, (genome in the future))
• No feed back to medical community and society

Input - Output
Process
Workflow
Information
Clinical Desicions
Action
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Cure for the pain points wave 1

• PAS Patient Adminstration System
• HIS Hospital Information System
• Result Distribution

Input - Output
Process
Workflow
Information
Clinical Desicions
Collect
Action
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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
Workflow
Information
Clinical Desicions
Optimization
Collect
Desicion support
Action
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Cure for the pain points wave 3

• feature extraction from unstructured or massive
  information (images, free text)
• Advanced connectivity
• Content
• Workflow optimization
• Intelligent patient portals
• Remote data capture
• Community HealthCare

• Information filtering
• Decision support
• Semantic driven UI
• Clinical Pathways
• Evidence based medicine
• Clinical Trials (in- and exclusion criteria,
  data mining)
• Terminology

Common to all this is
Input - Output
Process
Workflow
Information
Clinical Desicions
Optimization
Knowledge
Desicion support
Action
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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!

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Simple ontology
hobbies
Religion
Audi
Opel
Other Brands
Salary
Me
Model of
Instance of
A3
A4
owns
ABC 1234_567
Audi
A6
Green
has color
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Knowledge traditionally assumed
visit
Aspirin
?
Lab Test
Tenormin
hypertension
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Connected Knowledge explicit
visit
Conclusion of
Aspirin
Lab Test
Tenormin
Indication for
hypertension
threated by
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Connected Knowledge scalable
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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

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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 Appendicitis
Concept Appendix
inflamation of
Code XYZ
Term for
Term for
ICD9 code for
Term Appendix
Term Blindedarm
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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

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Agfas 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

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Adaptable Clinical Workflow Framework
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Adaptable Clinical Workflow (compare to GPS)
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Adaptable Clinical Workflow (compare to GPS)
After deviation from the calculated course the
system adapts the itinerary
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From pixel to community
The box is a fractal unit that can be scaled from
pixel to community
Human Interaction
Guidelines Policies Clinical Data Events Requests
(Local, Operational, Community, ...)
Recommendation
Desicion
Action
Desicion support
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Country ? World ? Healthcare Management
Region ? Disease Management
Institution ? Clinical Pathway
Department ? Order
Workstation/User ? Task
Application ? Event
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communication and event bus share knowledge and
evidence
Country ? World ? Healthcare Management
health monitoring process
form generator
clinical decision process
workflow monitoring process
task process
scheduling process
work list process
Region ? Disease Management
Institution ? Clinical Pathway
Department ? Order
Workstation/User ? Task
Application ? Event
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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

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Duplicate examinations

• CP 12
• 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

• 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

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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

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Bad sequences

• CP 12
• Day 1
• CP1_Action1
• CP2_Action1
• Day 2
• RXcontrast
• CP2_Action2
• Day 3
• CP1_Action3
• RX
• Day 4
• CP1_Action4
• CP2_Action4

• CP 1
• Day 1 CP1_Action1
• Day 2 RXcontrast
• 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

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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

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Wrong conclusion

• CP RheumaGU
• Rule x
• Rule If pain ? Aspirine
• Rule y
• Rule a
• Rule b
• Rule

• CP Rheuma
• Rule x
• Rule If pain ? Aspirine
• Rule y

• CP Gastric Ulcus
• Rule a
• Rule b
• Rule

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Wrong conclusions

• Because of the specific focus when making a
  clinical pathway, merging CPs 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

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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

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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.

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Thanks