4. Mission statement
create lasting and positive impact on society through IT innovation
improve quality and cost effectiveness in health care through
computational research and IT development
Health Decision Support for Professionals . Patients . Policy
• Dialogue with health care stakeholders
• Demand-driven & interdisciplinary
• Economical & societal valorization
reference center for computational research in health care
5. Societal trends
Ageing
60plus age related &
Flanders 2012
chronic diseases
rise
challenge: reconcile quality with costs
Patient empowerment P4 Medicine
• Personalized
• Preventive
• Predictive
• Participatory
challenge: patient-centric &
personalized health care
6. Technological trends
IT + internet Monitoring & Biomedical
performance & smart systems technologies
pervasiveness
• Variety of imaging
modalities
Moore’s Law • Multichannel,
Wireless, Mobile &
computing
power Real-Time
Carlson’s law
$1000 personal genome
broadband capacity & Biobank
pervasiveness
data access and
sharing
7. Challenge: data tsunami
interpretability complexity
# data, e.g. genetic
analysis bottleneck
Price per data point, e.g
sequenced base pair
IT to the rescue!
IT, mathematical engineering and software design
fully exploit the opportunities created by advancing technologies
8. Research focus: Health Decision Support
data: clinical, biomedical, imaging,
omics, health insurance data, • IT & software design
medical knowledge … • data processing & mining
• data integration & visualization
exploit data for more • user experience & e-learning
effective medicine
Clinical
patient-centred care for to extract appropriate
empowered patients information
Patient
smart & data-driven to transfer this information to
health care system policies the user:
Policy professional, patient and policy
maker
decision support to enable better health care
9. Research focus
Clinical Decision Support Patient Policy
DS DS
• interpretation of wide range of data
• demand-driven, user-centred, with future vision
10. Research focus
Clinical Patient Decision Support Policy DS
DS
‘online’ • data mining to
telemonitoring identify best
practices, …
• hospital logistics
‘patient empowerment’:
e.g. disease management
for patients with chronic
diseases using new media
11. Research focus: positioning
Translational
5-10 years ahead of
deployment
Health Care Health Decision Fundamental
Information Support Biomedical
Systems Research
Clinical Decision
Invoicing Support Pathogenesis
PACS/CWS Patient Decision Biomarkers
Support
Health Insurance Target/Drug
(e.g. RIZIV) Policy Decision Discovery
Support
12. Research focus: track record
in silico
drug
discovery image quantification
data handling & mining
for clinical genetics
medical image computing
social interest software
home monitoring for
(e-)learning solutions epilepsy detection
vision software & hardware Theraplay: ergotherapeutic game
13. Research focus: track record
• 17 PI, 25 postdocs, 88 PhD students
• ~ 270 publications /year
• ~ € 5 million external financing /year
• ~ 15 PhDs /year
• ~ 40 patents
• 5 spin-offs launched since 2005
15. Interaction with stakeholders
key to accessible and efficient health care = dialogue
between research, technology and health care
research
universities & research centres
strategic research centers: IBBT, imec, VIB, VITO
…
technology providers
& core facilities
health care stakeholders dialogue
industry
patients
supercomputing
hospitals IBBT
medical imaging center
doctors Future Health
usability lab
health insurance
sequencing (Genomics Core)
health care policy
…
…
16. Conclusion
improve health care quality and
Trends cost effectiveness
Decision Support for
Professionals . Patients . Policy
exploit data for more • Dialogue
effective medicine
Clinical
• Demand-driven
patient-centred care for
empowered patients
Patient • User-centred
smart & data-driven
Policy health care system policies • Future vision
19. Towards evidence-based and personalized
medicine
IBBT Future Health
high
personalized
access to (based on people like me)
patient
information
evidence-based
(based on population)
evidence
clinician
based
consensus
trial and error
individual
clinical knowledge
and experience
low
low high
access to clinical knowledge
(e.g. diagnostic tools)
