Bringing the Pieces
Together
Dr. Dirk Colaert MD
Advanced Clinical Application Research
Manager
The Healthcare paradigm shift...
Yesterday
Workflow
Diagnosis &
Treatment
Today and Tomorrow
Integrated & automated
Fragmented
The bottom line:
Invasive, often acute
Less invasive, imagebased, lifetime care
Increase quality
Focus
Provider-centric
Patient-centric
Follow-up
Hospital based
Decentralized, community
based
Lower cost
…Towards a decentralized healthcare model
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Dr. Dirk Colaert MD
The Building Blocks of the Clinical Process
Information
3
Assessment
Activities
collect, distribute,
share
think, decide
plan, schedule,
act, collaborate
Information
 Reasoning
 Workflow
Dr. Dirk Colaert MD
Bringing the pieces together
plan, schedule,
act,
collaborate
Clinical
Process
 Workflow
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Dr. Dirk Colaert MD
Scaling out into e-Health
Information
Decision Support
Rules and reasoning
Clinical Workflow
Workflow + collaboration
Phase 1
Phase 2
Phase 3
Shared Pathways
e-Health
Disease Mgmt. Programs
individual
Drug interactions, monitoring, compliance, alerts, … Health Monitoring
Inter-professional collaboration
e-Prescribe, CPOE
Personal Health Record (PHR)
Personal Health Plan
Shared Information (EHR) + collaboration
Population monitoring, epidemiology, Bio surveillance
Research from bench to bed, from bed to bench
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Dr. Dirk Colaert MD
Electronic Health Record
Health Mgmt. Programs
Translational Medicine
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Information
Decision Support
Workflow
Information  Connected Knowledge
•
Knowledge
•
•
•
Semantics
•
•
•
•
Rules about rules, policies
Proof and trust
•
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A set of rules makes a ‘theory’ (no standard yet, RIF?)
Unifying Logic
•
•
Formal description of a domain using concepts, facts and
relations
standards: OWL, RDF
Connected knowledge: merging of ontologies - the value of
the sum is bigger than the sum of the values !
Rules
•
•
A set of web technologies and standards to use,
communicate knowledge and pertain meaning
Ontologies
•
•
“sema”  sign  significance  meaning
The Semantic Web
•
•
Data  information  knowledge
 Intelligence  Latin: inter+legere  “connect between”
The ultimate data exchange
Dr. Dirk Colaert MD
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
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Dr. Dirk Colaert MD
Knowledge: traditionally ‘assumed’
visit
Aspirin
?
Lab Test
Tenormin
hypertension
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Dr. Dirk Colaert MD
Connected Knowledge: explicit
visit
Conclusion of
Aspirin
Lab Test
Tenormin
Indication for
hypertension
threated by
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Information: anytime, anyplace
Government
Home
Insurance
Home device
Sensor
Other care providers
Laptop
XDS Rep
Pharmacyst
Security
XDS Reg
Data
Local Doc
Store
MPI
Hospital
XDS Rep
Hospital
CIS/HIS
Data
Local Doc
Store
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CIS/HIS
Data
Data
XDS Rep
GP
Hospital
CIS/HIS
CIS
Data
Local Doc
Store
Dr. Dirk Colaert MD
Local Doc
Store
Information @ Home  Home Devices
Internet
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Dr. Dirk Colaert MD
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Information
Decision Support
Workflow
Medical Knowledge
Assessment
Medical
Knowledge
Shows why and how it works
Rule deduction
Bayesian networks
Shows that it works
Rule translation
Rule deduction
Clin. Pathways
Guidelines on
paper
Med. Science:
Pharmacology,
Physiology,
Genomics,
Anatomy,
Biochemistry, …
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Medical
Evidence
Clinical Data
genome
Patient
proteome
Images
…
Clinical Trials
Rule induction
New hypotheses
Dr. Dirk Colaert MD
Reasoning
• Clinical knowledge + medical knowledge +
reasoning  decision support  generation of
clinical pathways
• Reasoning engine: Agfa’s Euler engine (open
source)
• Seamless integration of deductive and Bayesian
inference
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Dr. Dirk Colaert MD
Decision support
Guidelines
Human Interaction
Policies
Clinical Data
Events
Requests
Request for Support
Recommendation
(Local,
Operational,
Community,
...)
Desicion support
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Dr. Dirk Colaert MD
Desicion
Action
Decision support is fractal (scalable from pixel to community)
Country  World  Healthcare Management
Region  Disease Management
Institution  Clinical Pathway
Department  Order
Workstation/User  Task
Application  Event
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Dr. Dirk Colaert MD
hobbies
Religion
Issues when merging ontologies
Salary
Audi
Opel
Other Brands
Me
Model of
owns
• Inconsistencies
Audi
A3
A4
Instance of
ABC 1234_567
A6
• Ontologies are build without other ontologies in mind. When
merged they can contain contradictions.
has color
Green
• This can be detected and brought to
the attention of the user.
• Semantic differences
• See the example above 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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Dr. Dirk Colaert MD
Duplicate examinations
• CP 1
•
•
•
•
• CP 1+2
• Day 1
Day 1 CP1_Action1
Day 2 Lab test: RBC
Day 3 CP1_Action3
Day 4 CP1_Action4
• CP1_Action1
• CP2_Action1
• Day 2
• Lab test: RBC
• CP2_Action2
• CP 2
•
•
•
•
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• Day 3
Day 1 CP2_Action1
Day 2 CP2_Action2
Day 3 Lab test: RBC
Day 4 CP2_Action4
• CP1_Action3
• Lab test: RBC
• Day 4
• CP1_Action4
• CP2_Action4
Dr. Dirk Colaert MD
Bad sequences
• CP 1
•
•
•
•
• CP 1+2
• Day 1
Day 1 CP1_Action1
Day 2 RX+contrast
Day 3 CP1_Action3
Day 4 CP1_Action4
• CP1_Action1
• CP2_Action1
• Day 2
• RX+contrast
• CP2_Action2
• CP 2
•
•
•
•
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• Day 3
Day 1 CP2_Action1
Day 2 CP2_Action2
Day 3 RX
Day 4 CP2_Action4
• CP1_Action3
• RX
• Day 4
• CP1_Action4
• CP2_Action4
Dr. Dirk Colaert MD
Wrong conclusion
• CP Ischemia
• CP Ischemia+GU
• Rule x
• Rule: … Aspirine
…
• Rule y
• Rule a
• Rule b
• Rule …
• Rule x
• Rule: … Aspirine
…
• Rule y
• CP Gastric Ulcus
• Rule a
• Rule b
• Rule …
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Dr. Dirk Colaert MD
Bayesian Believe Networks
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Dr. Dirk Colaert MD
Bayesian Believe networks
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Dr. Dirk Colaert MD
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Dr. Dirk Colaert MD
I
Information
Decision Support
Workflow
Clinical workflow is iterative and adaptable
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Dr. Dirk Colaert MD
Clinical workflow is iterative and adaptable
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Dr. Dirk Colaert MD
Clinical workflow is holistic
Adaptable Clinical Workflow
Information
Assessment
Quality
Efficiency
Control
Medical
Knowledge
Rule deduction
Bayesian networks
Activities
Practice
Clinical
Knowledge Knowledge
Operational
Knowledge
Rule deduction
Rule translation
Clin. Pathways
Theoretical
World
Semantic
World
Guidelines on
Medical
Med. Science:
Pharmacology,
Physiology,
Genomics,
Anatomy,
Biochemistry, …
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paper
Real World
Orders
Appointments
Executional World
Evidence
Clinical Data
Machines
Hospital Care
Providers
Patient
Images
Pharmacist
Rooms/Beds
...
GP
Clinical Trials
Rule induction
New hypotheses
Dr. Dirk Colaert MD
Semantic world: Rules and Ontologies
• Example of generic rule:
• If a diagnosis is an exclusion for process X and this
diagnosis is assessed by process Y and if the
diagnosis is not known, then process Y is a necessary
step.
• Clinical example:
• Pregnancy is assessed by HCG-labtest
• Pregnancy is an exclusion for CT
•  if a CT is needed HCG-labtest will be inserted in the
workflow
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Dr. Dirk Colaert MD
Hip fracture case
• Specific medical knowledge
• Orthopedist:
• If the patients complains about the hip you can do a CT or a Physical
Examination
• If the outcome of the reading gives “fracture” (<=0.5) then rehabilitation must
follow else surgical treatment
• If the assessment gives good result (>=0.5) then rehabilitation else redo the
surgical treatment
• General
• Surgical treatment consists of: pre-surgical exam, anesthesia, surgical
intervention and assessment
• Anesthesia must be completed before starting the intervention
• Pre-surgical exam must be completed before starting the intervention
• Intervention must be completed before starting the assessment
• + Radiation Protection Guidelines + gynecology + lab
• Patient state
• Medical context with Jane as patient and a CT reading giving the
diagnosis of hip fracture (0.7-0.9)
• outcome
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Dr. Dirk Colaert MD
Generated output for hip case (partial)
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Dr. Dirk Colaert MD
Drawbacks of Classical Clinical Pathways
• Static or limited dynamicity
• Standard procedures  not patient specific
• Doesn’t take into account the most current
operational and medical knowledge
• Procedural (as opposed to declarative)
• Merging issues, maintainability
• These are also pretty much the disadvantages of
many generic workflow systems
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Dr. Dirk Colaert MD
The executional world
• Goal
• Holistic behavior
• without central control
• without the need for one database
•
•
•
•
Scalable
Extendable
Smart
Non disruptive (embed existing stuff)
• Method:
• Peer to peer processes with following capabilities:
• Reasoning (driven by KPI’s)
• (abstracted) Communication of knowledge
• Rooted in the executional world
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Dr. Dirk Colaert MD
 think
 talk
 do
Federated workflow: choreography of processes
communication and knowledge bus (Web Services, HTTP Get/Post, XDS, email, …)
Executional data: instance data, real world
workflow
form generator work list process
monitoring process
task process
scheduling
process
clinical decision health monitoring
process
process
Semantic world:
ontologies, rules, logic framework, proof
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Dr. Dirk Colaert MD
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
health monitoring clinical decision
process
process
Workstation/User  Task
scheduling
process
workflow
workflow
monitoring process task process
monitoring process
work list process form generator
scheduling
process
task process
work list process
Application  Event
form generator
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Dr. Dirk Colaert MD
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Bringing the pieces together
Bringing the pieces together
plan, schedule,
act, collaborate
 Workflow
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Dr. Dirk Colaert MD
The e-Health Infrastructure
Hospital
Polyclinic
Government
Pharmacy
Patients
GP
eHealth
infrastructure
A central infrastructure enabling e-Health
This can but doesn’t need to imply that
data is physically persisted in a central
storage
Laboratory
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Dr. Dirk Colaert MD
The bottom line: a user should have a
consolidated and complete view on the
clinical data of the patient
Knowledge
Sharing
Workflow
Phase 2
Information
sharing
Infrastructure
E-Health
E-Health
Phase 3
Monitoring
Phase 1
3: Services
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2: Messaging
1: Infrastructure
Dr. Dirk Colaert MD
Aggregation services: patient
summary, drug prescription
Monitoring services:
interaction checking, harm
detection, alerts
Decision Support services:
recommendations, guidelines
Workflow services: care plans,
chronic disease management,
personal health plans
Statistical and
epidemiological services
Financial services,
Administrative services, …
Subscription or transaction based
Installation and maintenance of
servers, network, databases
usability, added value
The goal: e-Health
The Roadmap to e-Health
Health Management Programs
Phase 3
Personal Health Plans
Disease Management Programs
Workflow
Monitoring
Information
sharing
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Messaging based
on ontologies
Virtual EHR
Share simple
documents
Infrastructure
Monitoring and
alerts
Home devices
Patient Portal
Phase 1
E-Health
Phase 2
Adaptable pathways
Privacy
Informatio
n
Workflow ontologies
Procedural
pathways
Workflow
Dr. Dirk Colaert MD
Add clinical content (rules)
Knowledge
Sharing
Other ontologies: genomics, images,
physiology virtual human body
The goal: e-Health
The Roadmap to e-Health
Clinical ontologies
Terminology
Standards
(UMLS, SNOMED?)
Decision
Support
Bringing the pieces together
Let’s just do it . . .
plan, schedule,
act, collaborate
 Workflow
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Dr. Dirk Colaert MD
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