The document describes a design study methodology for developing interactive visualizations to support personalization of health data for patients and users. It involves 3 phases - a pre-condition phase to understand the domain through literature review and expert input, a condition phase to design and implement visualization prototypes through an iterative user-centered process, and a post-condition phase to deploy and validate the solutions. The methodology supports achieving the objectives of understanding healthcare needs, designing interactive visualizations, and validating the solutions with experts.

1. Design Study
Methodology
for Health
Industry 5.0
Dr Mithileysh Sathiyanarayanan
Founder | CEO | Scientist

2. What is Health Industry 5.0?
2
Let’s quickly understand
 Industrial Revolution
 Health Industry Revolution

3. Industrial Revolution
3
Industrial Revolution
refers to the interaction between
people and machines to make
work better and faster.
Industry 1.0 to 4.0 is known as
Industrial Revolution.

4. 4
Focus:
Mechanics
Strategic
Differentiator:
Mechanisation /
Steam Power
Focus:
Electricals
Strategic
Differentiator:
Mass
Production
Focus:
Electronics &
Computers
Strategic
Differentiator:
Mass
Automisation
Focus:
Cyber-physical
Systems
Strategic
Differentiator:
Mass
Customisation
INDUSTRY
1.0
PRODUCTION
INDUSTRY
2.0
INDUSTRIALISATION
INDUSTRY
3.0
AUTOMATION
INDUSTRY
4.0
DIGITALISATION
Industrial Revolution
Eg. Steam
Engines
Eg. Electrical
Machines
Eg. Robotics Eg. Smart
Watches
Focus:
Customer-
centric
Strategic
Differentiator:
Mass
Personalisation
INDUSTRY
5.0
PERSONALISATION
Eg. Superfast
Manufacturing

5. 5
Focus:
Evidence-based
Treatment
Model
Strategic
Differentiator:
Patient
Survivability
Focus:
Value Chain
Integration
Model
Strategic
Differentiator:
End-to-end
Service
Coverage
Focus:
Operating Model
Strategic
Differentiator:
Cost to Serve,
Efficiency
Focus:
Business Model
Strategic
Differentiator:
Mass
Customisation
Focus:
Customer Model
Strategic
Differentiator:
Mass
Personalisation
Quality of Life
HEALTHCARE
1.0
PRODUCTION
HEALTHCARE
2.0
INDUSTRIALISATION
HEALTHCARE
3.0
AUTOMATION
HEALTHCARE
4.0
DIGITALISATION
HEALTHCARE
5.0
PERSONALISATION
Health Industry Revolution

6. What is Health Industry 5.0?
Also, called as
 Healthcare 5.0
 Health & Industry 5.0
6
Health Industry 5.0 provides a product to the
customer as per their specific
requirements.
The complete focus is on healthcare
professionals and patients.
Their lifestyle, wellbeing, health analytics,
quality of life etc.

7. 7
 Apple’s watch is paired with AI-based software, it
can predict high blood pressure and sleep apnea.
 Google-purchased AI startup, DeepMind, which is
aiming at changing the way diagnosis and
treatment are performed in hospitals.
 Amazon is exploring telemedicine applications
with medical supply chain that will be highly
disruptive.
Health Industry 4.0 to 5.0

8. Success of Healthcare 5.0
8
ACADEMIC
RESEARCH
3
HEALTHCARE 2
INDUSTRIAL
COLLABORATION
1

9. Health Industry 5.0
Personalisation of Health Data
9

10. Research
Question
To what extent visualisations
can support patients/users in
finding relevant and/or
interesting information in their
own personal health data?

11. Overall Aim
The aim of the research is to
design and develop interactive
visualisation platform that can
be personalised to explore and
find relevant/interesting
information in a personal health
record.

12. Objectives
O1: Understand the healthcare
domain & review literature.
O2: Design & develop interactive
visualisations
O3: Validation of visual methods
O4: Iterative refinement of the
developed prototype.

13. Design
Study
Methodology

14. 14
Design Study Methodology (DSM)

15. Design Study Methodology (DSM)
Real-world Problem
Real Users
Real Data

16. DSM – Precondition Phase
(Phase 1)
16
STAGE 1 : LEARNING
STAGE 2 : WINNOWING
STAGE 3 : CASTING

17. Stage 1: Learning
Visualisation features - we identified four main features in
visualising healthcare data, that is temporal, individuals and
contents, including thread features.
Visualisation techniques/methods - more than half the
surveyed papers use conventional visualisations (basic charts
and matrices) to personalise data.
Visualisation tasks/interaction system - almost half of the
surveyed papers only provide an overview and show their analytic or
visual results but do not implement a details-on-demand and
exploration functionality.

18. Stage 2 & 3:
Winnowing &
Casting
Industry Collaborators
Academic Researchers /
Principal Investigators
Healthcare Professionals /
Patients / Users

19. DSM – Condition Phase
(Phase 2)
19
STAGE 4 : DISCOVERING
STAGE 5 : DESIGNING
STAGE 6 : IMPLEMENTING

20. Problem Characterisation & Abstraction
Finding
interesting
conditions
within the
large volume
of health
data
Complex and
dynamic nature
of health
patterns
Open-ended
data
exploration to
find
interesting
health
condition
patterns
Stage 4: Discovering
3 Design
Challenges

21. Data & Task Characterisation & Abstraction
10
DESIGN
REQUIREMENTS
3
ANALYSIS
GOALS
10
TASKS
2
DATASETS
3
CASE STUDIES
Stage 4: Discovering

22. Stage 5 & 6:
Designing & Implementing

26. DV PHASE 1:
TEMPORAL
ANALYSIS

27. DV PHASE
2:
INDIVIDUAL
ANALYSIS

28. DV PHASE 3:
THREAD
ANALYSIS

29. DSM – Post-condition Phase
(Phase 3)
29
STAGE 7 : DEPLOYING
STAGE 8 : REFLECTING
STAGE 9 : WRITING

30. 30
Stage 7:
Deploying

31. ⬡ Iterative user-centered design approach gave us a good base to
capture user requirements (from the experts) which helped us achieve
Objective 1 (O1).
⬡ Iterative user-centered design approach gave us a good platform to
design interactive visualisation solutions with the experts which helped
us achieve Objective 2 (O2).
⬡ Iterative user-centered design approach also gave us a good space to
validate our solutions with the experts which helped us achieve
Objective 3 (O3).
Stage 8: Reflecting

32. F1: An iterative user-centered design approach helped in understanding
E- discovery domain and the investigation needs:
F2. Multi-faceted exploration and multi-granular analysis helped in
discovering interestingness:
F3: Interactive visualisation assisted active learning helped in classifying
communications:
Findings

33. L1. Domain-specific requirements in depth should be focussed.
L2. Conventional visualisations and novel visualisations should be
carefully analysed.
L3. Evaluation with non-experts should be considered.
Learnings

34. P1. Focus on domain-specific requirements.
P2. Consider iterative user-centric design throughout design-cycle.
P3. Generate system-based features for pattern characterisation.
P4. Build pattern-oriented interactive visualisations for discovering
interestingness.
P5. Leverage multi-facetedness and multi-granularity for exploration and
discovery.
P6. Important to represent evolution of healthcare data.
P7. Evaluate the system with both experts and non-experts.
Principles

35. ⬡ Characterised the domain, problems and tasks for Healthcare 5.0.
⬡ Identified knowledge gap and provided overview of the existing techniques.
⬡ Designed and Developed interactive visualisation solutions.
⬡ Validated the solutions
⬡ Deployed Solutions in the Collaborator’s Platform (Google Suite).
⬡ Lesson Learnings & Principles
⬡ Presented papers at various venues India, USA, UK & Germany
⬡ Filing a Patent in the USA.
Stage 9: Writing / Contributions

36. About
MIT SQUARETM
36
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37. Our Global Presence
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40. 40
Thanks!
Any questions?
You can find me at:
mithileysh@mitsquare.com