Presentation done at #WHOisDigital23 in Oporto, 6th Sept 2023 in the table From Big Data to Personalized Care: The Evolution of AI and Precision Medicine, Vicente Traver is presenting how data can be used for personalized care, also for prevention and not just for diagnosis, making emphasis on how we can also measure different variables in a non obstrusive way and using indirect approaches.

1. From Big Data to Personalized
Care: The Evolution of AI and
Precision Medicine
BIG DATA AND PERSONALIZED CARE:
How massive streams of health-related big
data, derived from diverse sources have the
potential to drive and refine personalized care?
Vicente Traver
Universitat Politécnica de Valencia. Spain
vtraver@itaca.upv.es

2. Big Data
Public
Health
Personalized
Care
2

3. Big Data, Open Data, Open Government
3
They are not the
same but they can
help to reach our
GOALS

4. Personalized care supported by big data implies…
More accurate
diagnosis
Better
treatment
adherence
Behavior change
towards healthy
lifestyles
Value based
healthcare
4

5. IoT and personalized care beyond the person
5
Urban health

6. What is behind IoT
6
• Integration with existing IoT environments
• Smart homes, hospitals, cities
• Ethical and privacy issues
• Wearables and audible
• Another way of interaction
• Generation of new ecosystem
• Option to get access to elderly with limited IT skills

7. Joining healthcare and wellbeing with the world through IoT
7
Active and health ageing
Mental health
Medical monitoring (children, elderly, drivers)
Safety interventions (firemen, police…)
Workplace environments (e.g. worker well-being and safety in harsh environment)
Connected/autonomous automotive (driver and passenger interaction during travel).

8. Personalized care – getting the data
Direct or indirect measurement?
8
Data Information Knowledge
Decision
making at the
right moment

9. Non health related
data can provide us
useful insights
9

10. 10
https://healthmap.org/en/

11. Sources for data collection
• Explicitly, directly through questionnaires or user ratings
• Implicitly through observing user-system interaction and user behaviour
• User activities and states from accelerometer, audio & location data
• Social environment and social ties from audio data & proximity sensing (e.g.
BT)
• Interests e.g. from places visited, documents viewed, apps used, social
network profiles, e-mails, SMS and calendars
• Knowledge level e.g. from answer’s to exercises, time spent reading
something, number of actions required to perform a task
• Vital signs measurements
• Personal Health Record (PHR), Electronic Health Record (EHR),
consumption of healthcare services
@Honka, VTT IEEE EMBC within PREVE project

12. How to measure the mood of a person?
12
Questionnaire Voice GPS Accelerometer
Bluetooth Text written Readings

13. Citizen behavior and health outcomes are closely linked
• Behavioral changes would significantly reduce life style diseases and improve
wellbeing
• 70–90% of cardiovascular disease, type II diabetes and stroke would be avoided (Willet, 2002)
• Individuals with healthy lifestyles:
• 50% reduction in health care costs (Pronk et al., 1999)
• 14 years longer expected life time (Khaw, 2009)
• Exercise acts as a drug; the pharmacological benefits of exercise (Viña, 2012)
• Behavioral change requires interventions to lifestyles
• Life style changes cannot be prescribed – they have to be marketed and promoted
• Life style is very personal and impacted by complex motivational and value-based factors
• Personal health systems facilitate behavioral change
 Actual health outcomes are largely produced by the citizen behaviors – not just by health care
procedures (alone)
Determinants of
health status
Health Care
(10%)
Environmental
(5%)
Behavioral
(40%)
Social
(15%)
Genetic
(30%)
McGinnis et al., Health Affairs 21(2), 2002

14. Profiling for personalized care
Person
• Long term
intentions,
needs,
preferences
• Values, attitudes
Independent
context
• Current situation
and surroundings
• Events during the
day
Context depending
on the person
• Current health
status, feeling,
needs
• Habits, available
resources
Profile is context and DATA –dependent … evolving along the time
DATA

15. Key messages to take home
15
Go beyond eat
healthy, do
physical
activity...
Personalisation
is key to
produce value
based healtcare
Health and non
health related
data can help
us to
personalise care
Leaving nobody
behind ...
ADOPTION is
KEY!

16. Challenges for the future
Data curation Interoperability
Traceability
16

17. From Big Data to Personalized
Care: The Evolution of AI and
Precision Medicine
BIG DATA AND PERSONALIZED CARE:
How massive streams of health-related big
data, derived from diverse sources have the
potential to drive and refine personalized care?
Vicente Traver
Universitat Politécnica de Valencia. Spain
vtraver@itaca.upv.es