Digital Revolution in Healthcare System

Human centered data management
- technical and regulatory aspects

Digital Health Revolution (2014):
´Fundamental revolution is needed to truly enhance
the proactive, personalized health maintenance for
the benefit of individuals, society and business.
The vision of Digital Health Revolution -initiative is
that future health care will allow citizens to control
and make use of his or her personal data.’

MyData and Regulation - Privacy
• Privacy and Data Protection are a
global Fundamental Right
• Personal Data Processing
interferes with these
Fundamental Rights
• All Personal Data Processing
needs adequate justification
Personal Data
All information relating to an
identified or identifiable natural
person.
Data Concerning Health
Personal data related to the
physical or mental health of a
natural person, including the
provision of health care services,
which reveal information about his
or her health status.

Health Data
• is a special category of personal data
• is sensitive and may carry a high risk to right and freedoms of individual
• Health Data Processing is generally prohibited unless there is an adequate
justification:
• Health data processing is necessary to protect the vital interest when a
person can’t give consent.
Vital Interest
• Necessary for the purposes of preventive or occupational medicine,
medical diagnosis, the provision of health or social care or treatment or the
management of health or social care systems and services
• Processed by, or under the responsibility of a professional subject to the
obligation of professional secrecy under Law
Public Interest and
Health Care Services
regulated by Law
• Individual’s freely given, specific, informed and unambiguous consent
• Explicit consent for a specific purpose
Explicit Consent

DHR needed to create a Platform that implements
existing Legal Standards
Follow the DP principles: fair, lawful, legitimate purpose,
transparent, secure, accurate.
Adequate legal basis for processing of special data.
Enable proper explicit consent.
Take risk-based approach: consider risks to rights
and freedoms.
Enable all data subjects rights (access, control,
transparency etc).
Build secure systems, privacy by design, and by
default.
From ‘Fundamental Revolution’ to the implementation of fundamental
rights into a MyData ecosystem.

DHR needed to create a Platform that implements
existing Legal Standards
Follow the DP principles: fair, lawful, legitimate
purpose, transparent, secure, accurate.
Adequate legal basis for processing of special data.
Enable proper explicit consent.
Take risk-based approach: consider risks to rights and
freedoms.
Enable all data subjects rights (access, control,
transparency etc).
Build secure systems, privacy by design, and by
default.
From ‘Fundamental Revolution’ to the implementation of fundamental
rights into a MyData ecosystem.

Attempt to define a Reference Architecture
- Phase 1 (2014-16)
Covers MyData White Paper’s master use case: explicit
consent for 3rd party data (re)use

Authorisation
Consent Records
Service Linking
Link Record
2.
Data Transfer
Transfer Log
4.
3.
D: Data Sink
Uses data to produce
new applications,
services and
information
C: Data Source
Provides static or
dynamic data, can be
original source or
secondary Data Store
A: MyData (Consent) Operator
Provides MyData Accounts and
related services
B
A
DC
B: Account Owner
Individual Data Subject

Reference Architecture - Phase 2 (2016-2017)
Continued strong emphasis on enabling data
subject’s dynamic consent
Consenting
& adjustment

Contract and consent as legal bases of processing
Consenting
& consent
adjustment
Consent A Consent B
Data
flow
Contract

Public interest and objection (opt-out) legal bases
Notification Objection

More materials at
http://sandbox.mydata.fi
http://bit.ly/mydata_arch_frmwk_20

Minna Pikkarainen &
DHR Business Research Team
Business models and MyData
- Summary of Business Research

Lähde (muokattu): Poikola, Kuikkaniemi & Honko. 2015. MyData - A Nordic Model for human-centered personal data management and processing.
Suom.: MyData - johdatus ihmiskeskeiseen henkilötiedon hyödyntämiseen
Saatavilla LVM:n sivuilla: https://www.lvm.fi/documents/20181/859937/MyData-nordic-model/2e9b4eb0-68d7-463b-9460-821493449a63?version=1.0
MyData-infrastructure
Open business environment

Research Settings 1
Alexandra Institute
Coelition
Cozy Cloud
Ctrl-Shift
Databox
Data Mixer
Digi.me
Digital Catapult
Fing
Forgerock
Healthbank
Lynkeus
Meeco
Midata coop
Mydex
MyWave
Open Notice
OpenPDS / MIT
Peercraft
Project Danube
Qiy
Sunderland City Council
Synergetics
Telefonica
Telekom Italia
Tisei
Warwick Hub-of-All-Things

Data using service
Platform operator
Data source
Policy developers
Public
organizations
Access to own data
via a platform
Share own data
via a platform
Individual in control over data
Value co-creation
Actors and roles in MyData Ecosystem
Birth Health
Symptom
evaluation
Diagnosis
Care
monitoring
Care Exit
Interaction (data, consent & money flow)
Source: Laura Kemppainen

Going deeper - Research settings 2
Scenario interviews in which the concrete
Mydata scenario was presented for each player

Data using service
Platform operator
Data source
Policy developers
Public
organizations
Access to own data
via a platform
Share own data
via a platform
Interaction (data, consent & money flow)
Individual in control over data
Value co-creation
Business Research conducted
Platform operator perspective
(nro articles)
• Platform operator business
& revenue models
• Concrete support for
potential platform
organizations (Elisa,
Mehiläinen, Affecto,
Sonera)
Source & User research (nro
articles):
• Heltti Mydata in daily burnout
tracking (1)
• Hämeenlinna – Healthcare
service transformation due to
Mydata (1)
• Insurance companies - Mydata
& Business models for (1)
Individual perspective (nro
articles):
• Consumer acceptance for
Mydata approach (1)
Ecosystemic perspective (nro. articles)
• Success factors & Hinders (1)
• Revenue models (2)
• Data as a resource (1)
• Data valence (1)
• Ecosystemic business model (1)
• Mydata business model canvas

Benefits of
personal data management
(value proposition)
Value
co-creation
Individual
centered
Access
to data
Individual
centered
Benefits of data
integration
(value proposition)
ChallengesAccess to data &
utilization of data
Individual’s perspective in
business model transformation
Company’s perspective in
business model transformation
Transformative health & wellness business: personalized, predictive, preventive, participatory
Canvas to help organizations in the MyData
transformation

Bob goes to occupational
healthcare reception if
necessary
Bob receives wellbeing feedback and
guidance. An application will direct
him to healthcare reception if
necessary.
Bob starts wearing an activity tracker
provided by the employer. The data
will also be transferred to
occupational healthcare.
Bob’s manager gets a notification and a report
about signs of burnout among the staff. He
immidiately takes action to correct the
situation.
Example of Mydata solution – Case - Heltti & Affecto

Time
Open
Business
Environment
Human
centric
services
Usable data
Potential of MyData Transformation
• Data from 2000
customers
• Annual wellbeing survey
• Sleep and activity data
• Daily data acquired (e.g.
activity data)
• Employer-harvested data
integrated with wellbeing
and health data
• All data consented by
individuals for the use of
the service (e.g. shopping,
location, psychologist’s
evaluations)
• Virtual wellness service
for end-users
• Personalized continuous
analysis of burnout risks
• Personalized mentor-
application which guides
user in preventing burnout
• Solution informs managers if
there is a need to act on
burnout risks
• Personalized application
taking into account
financial aspects and
shopping behaviour, and
guides the user to e.g.
financial counseling or a
nutritional expert
• Employer organizations
pay for services aiming
for healthier workforce
• Separate contracts with
platform- and activity
tracker providers
• Contract with platform
operator -> access to
individual data
• Joining to trust network,
contract with platform
operators - > access to
multiple data via
individual consent
MyData principles Phase 1 Phase 2 Phase 3

Private
healthcare
service
providers
Platform
operator
Insurance
service
providers
Wellbeing
service
providers
Technology
and
application
service
providers
• Complementary
services
• Outsourced data
management
• New customer
channels
• Royalty-based
income and
smart contracts
• New customer
channels
• Health data
driven services
• More efficient
processes
• Personalized
services
• Transaction fees
-personalized
services
-convenience
Mydata ecosystems - near future
– Conclusion
Individual
Business benefit
Individual Data providers Data users
Service providers that
authorize the use of data
case by case with the
consent of the individuals

What is hindering the change? – Conclusion
• Legislation and
regulations
•The rules and
responsibilities of the
use of different types
of data sources are
unclear
• Privacy policy
• Data is siloed
• Lack of common data
interfaces
• Lack of systematic
data management
• Issues regarding data
reliability
• Roles, activities, and
business logic in the
ecosystem are inclear
• The benefits of data
sharing are obscure
• Limited collaboration
between different actors
• How to reach and involve
inactive potential
customers?
Regulation Technology and data Business environment

Recent articles
Published:
• Pikkarainen, M. Iivari M. 2017 Toward the concept of ecosystemic data-driven business models – case study in the area of preventive health care, Nordic academy of
management conference
• Laura Kemppainen, Minna Pikkarainen, Timo Koivumäki, 2017, Business Models for Platform Operators in the Field of Human-centered Personal Data Management:
A Case Study Approach, Nordic academy of management conference
• Iivari, M. Pikkarainen. M. Koivumäki, T. How MyData is transforming the business models for health insurance companies, Pro-ve 2017, accepted
• Huhtala, T. Saraniemi, S. Pikkarainen, 2017, M. Transformation of the Business Model in an Occupational Health Care Company in the Emerging Personal Data
Ecosystem - A Case Study in Finland, ICBIR 2017
• Pikkarainen, M. Punkka, E. Ailisto, H. VTT Blogi terveydenhuollon muutoksesta
• Pikkarainen, M. Terveydenhuollon muutos vaatii yrityksiltä yhteiskehittämistä, Oulu Business School julkaisu 2016
Coming:
• Koivumäki, T., Pekkarinen, S., Lappi, M., Väisänen, J., Juntunen, J., Pikkarainen, M. Consumer Acceptance of future mydata based preventive ehealth services, JMIR,
(resubmission)
• Pikkarainen, M. Huhtala, T. Iivari, M. Häikiö, J. Kemppainen, L. Success factors of data-driven service ecosystems in the context of preventive health care, Service
Science special issue, in 2nd round
• Kemppainen, L. Saraniemi, S. Koivumäki, T. Pikkarainen, M. Value creating roles of service platforms within personal data based service ecosystems, Service Science
Special issue, in 2nd round
• Pikkarainen, M. Pekkarinen, S. Koivumäki, T. Huhtala, Connected health service transformation from the perspective of digitalisation and consumerization, Journal of
Business Research, (re-submission)
• Häikiö, J. Ylikauhaluoma, S. Pikkarainen, M. Koivumäki, T. Iivari, M. Data valence in preventive healthcare ecosystems (work in progress)
• Huhtala, T. Preconditions for using data as a resouce in a preventive healthcare ecosystem (in finalising phase)

Contact: prof. Minna Pikkarainen
University of Oulu
minna.pikkarainen@oulu.fi

Program
13.30 Opening words & MyData in Healthcare: Maritta Perälä-Heape
13.45 Human centered data management - technical and regulatory aspects: Harri Honko & Jens Kremer
14.00 Business models and MyData: Minna Pikkarainen
14.15 Me and MyData: Jonna Häkkilä, Minna Ruckenstein, Timo Koivumäki, Miikka Ermes
14.40 P4 medicine and DHR pilot: Riitta Sallinen
15.00 - 15.30 Break: Video posters and demos
15.30 Greetings from Tekes: Pekka Sivonen
15.40 International Health Account Network: Madis Tiik
16.00 MyData and Blockchain in Health: Wilfried Pimenta de Miranda
16.20 MyData Nordic Whitepaper discussion: Bogi Eliasen & Maritta Perälä-Heape

Making MyData approachable
– User Interfaces
Jonna Häkkilä, prof.
Faculty of Art & Design
University of Lapland

Design process
• User research and state-of-the-art study
• Concepting workshops
• Co-creation with different stakeholders and designers
• Designing application or service concept
• Prototyping
• Interactive prototypes of mobile apps
• Industrial design mock-ups
• Video or experience prototype on key use cases
• Evaluation with a user study
• Controlled studies, e.g. usability tests in a lab
• In-the-Wild user studies in authentic use context

State-of-the-Art Analysis
Analysis of 39 most popular
wellness apps
Häkkilä, J., Colley, A., Inget, V., Alhonsuo, M., Rantakari, J. (2015).
Exploring Digital Service Concepts for Healthy Lifestyles. In Proc. HCI
International 2015, Springer.

Trend from data centric presentation
to goal and event based approaches
36
Benchmarking Wellness UI Designs

Charting the big picture…
Service design and co-creation
workshops
 Identifying new
opportunities

Creating storyboards, personas, stakeholder
maps

Creating mock-ups and use scenarios
More e.g. in: Häkkilä, J., Colley, A., Inget, V., Alhonsuo, M.,
Rantakari, J. (2015). Exploring Digital Service Concepts for
Healthy Lifestyles. In Proc. HCI International 2015, Springer.

Creating UI designs
40
1. Big number of alternative
design concepts
2. Narrowing down to the
final concept
3. Designing interaction flow
4. Polishing visual design

Examples of created UI designs

FIMM Dashboard UI Development
https://marvelapp.com/8ej2ag

Selected key results – user study on User
Interfaces
Colley, A., Halttu, K., Harjumaa, M., Oinas-Kukkonen, H. 2016. Insights
from the Design and Evaluation of a Personal Health Dashboard. HICSS
2016: 3483-3492
• Showing trends is more valuable
than granularity and detail
• The design must leave space for
users to make their own
discoveries
• More explicit instructional
content also needed
• Goal setting features
• Self-education – use with
professionals

Conducting user studies
Häkkilä et al., HICSS’16
Lappalainen et al., MUM’16
Colley et al., PerDis 2016

Example – Interviewing wellness coaching
professionals
Selected key findings:
• Cross-platform
operability between
systems a problem
• Need for holistic
tracking in an easy way
• Need for easy UIs for
communication
between client and
coach
• Professionals need a
large network of other
specialists
• Peer support importantHapuli, Jenni (Pro Gradu, Lapin yliopisto, 2017). Digitaalisesti
avustetun hyvinvointivalmennuksen kehittäminen
palvelumuotoilun keinoin

Examples - Preferences on Wearable Wellness
Devices
• Online survey with 84
participants
• Charting people’s
preferences on different
device features
• Form factor and ease-of-use
factors important
• Personalization, sharing and
context less prioritized
Häkkilä, J., Rantakari, J., Inget, V. Charting User Preferences on
Wearable Wellness Devices. In Proc. Augmented Human 2016. ACM.

Exploring new application and service
concepts

Exploring use contexts of the dual sided
tablet
• Tablet with two
screens
• Modified view
shown to the
patient
• Improves
• communication
• trust
Colley, A., Rantakari, J., Virtanen, L., Häkkilä, J. 2017. Mediating Interaction
between Healthcare Professionals and Patients with a Dual-Sided Tablet. In
Proc. INTERACT 2017. Springer.

UX publications – JuFo ranked
• Häkkilä, J., Colley, A., Inget, V., Alhonsuo, M., Rantakari, J. (2015). Exploring Digital Service Concepts for Healthy
Lifestyles. In Proc. HCI International 2015, Springer.
• Alhonsuo, M. Kuure, E., Miettinen, S. Service Design as a Learning and Prototyping Tool in a Healthcare Setting.
Unexpected Encounters – Senses and Touching in Services and Care 4th Encounters Conference 17-19 March
2015, Porvoo - Borgå, Finland.
• Colley, A., Virtanen, L., Väyrynen, J., Häkkilä, J. (2015). Physically Guiding Touch Screen Interaction with Public
Displays. In Proc. PerDis’15. ACM 2015.
• Colley, A., Rantakari, J., Häkkilä, J. (2015). Dual Sided Tablet Supporting Doctor-Patient Interaction. In Adjunct
Proc. CSCW’15 (demo).
• Rantakari, J., Colley, A., Häkkilä, J. Exploring AR Poster as an Interface to Personal Health Data. In Proc. MUM
2015
• Häkkilä, J., Alhonsuo, M., Virtanen, L., Rantakari, J., Colley, A., Koivumäki, T. MyData Approach for Personal Health
– A Service Design Case for Young Athletes. In Proc. of HICSS 2016.
• Colley, A., Halttu, K., Harjumaa, M., Oinas-Kukkonen, H. 2016. Insights from the Design and Evaluation of a
Personal Health Dashboard. HICSS 2016: 3483-3492
• Colley, A., Virtanen, L., Häkkilä, J. Guided Touch Screen – Enhanced Eyes-Free Interaction. In Proc. PerDis 2016.
ACM
• Häkkilä, J., Rantakari, J., Inget, V. Charting User Preferences on Wearable Wellness Devices. In Proc. Augmented
Human 2016. ACM.
• Lappalainen, T., Virtanen, L., Häkkilä, J. (2016). Experiences with Wellness Ring and Bracelet Form Factor. In Proc.
of the 15th International Conference on Mobile and Ubiquitous Multimedia (MUM) 2016. ACM.
• Colley, A., Rantakari, J., Virtanen, L., Häkkilä, J. 2017. Mediating Interaction between Healthcare Professionals and
Patients with a Dual-Sided Tablet. In Proc. INTERACT 2017. Springer.
• Colley, A., Marttila, H. 2017. Introduction to Service Design for Digital Health. In Proc. INTERACT 2017. Springer.

UX publications - other
• Häkkilä, J., Cheverst, K., Huuskonen, P. (2015). Who Needs a Doctor Anymore? Risks and Promise of Mobile Health
Apps. In the Extended Abstracts of MobileHCI 2015. ACM 2015.
• Häkkilä, J., Colley, A., Alhonsuo, M., Inget, V., Rantakari, J. (2015). Reflecting on Experiential Aspects of a Dental
Service Concept. In CSCW’15 workshop on Moving Beyond e-Health and the Quantified Self. March 14-15, 2015,
Vancouver, Canada.
• Alhonsuo, M., Hapuli, J., Virtanen, L., Colley, A., Häkkilä, J. (2015). Concepting Wearables for Ice-Hockey Youth. In
Adjunct Proceedings of MobileHCI’15.
• Häkkilä, J., Alhonsuo, M., Rantakari, J., Colley, A., Virtanen, L. (2015). MyData Collection for Personal Health: Concept
Design of a Lifestyle App for Junior Athletes. In Adjunct Proc. of Interact 2015.
• Inget, V., Rantakari, J., Väyrynen, J., Häkkilä, J. (2014). Charting the Current Trends in See-through Heads-Up
Concepts. In NordiCHI 2014 workshop on Interactions and Applications on See-through Technologies, October 26,
2014, Helsinki, Finland.
• Häkkilä, J., Virtanen, L. 2016. Aesthetic Physical Items for Visualizing Personal Sleep Data. In MobileHCI’16 workshop
on The Role and Impact of Aesthetics in Designing Mobile Devices. Florence, Italy. Sept 6, 2016. In Adjuct Proc. of
MobileHCI’16, ACM.
• Hapuli, Jenni (Pro Gradu, Lapin yliopisto, 2017). Digitaalisesti avustetun hyvinvointivalmennuksen kehittäminen
palvelumuotoilun keinoin

Contact: prof. Jonna Häkkilä
University of Lapland
jonna.hakkila@ulapland.fi

The datafication of health
- Key insights and future research
Consumer Society Research Centre /
University of Helsinki
Minna Ruckenstein, Mika Pantzar and
Sari Yli-Kauhaluoma

The datafication of health
Laurie Frick
• A field known as digital
health, eHealth, mHealth,
and Medicine 2.0.
• Takes place on multiple
scales and registers such
as biobanks, governmental
databases, clinical health
care, continuous patient
monitoring, implantable
biosensors, personalized
medicine practices

Thematic clusters
• Datafied power
• Living with data
• Data-human mediations (Ruckenstein & Schüll 2017)
Ruckenstein, M., Schüll, ND. 2017: The datafication of health. Annual Review
of Anthropology 46: 261-278.

Datafied power
• Concerns themes of exploitation, surveillance,
and neoliberal governance
• Companies monetize data voluntarily collected by
individuals for their own purposes by extracting
and combining it with others’ data to draw
population-wide correlations and inferences that
hold value on the market of health data
(Nissenbaum and Patterson 2016)

Living with data
• Takes an ethnographic, case-based view on engagements with data and its
technologies to illuminate data practices, datasociality, and self-fashioning through
data
• The data offers a common language that people can relate to, becoming “a medium of
connecting with others by offering a raw glimpse into one’s intimate private life”
(Sharon 2016b, 20), a form of open-ended communication (Lomborg and Frandsen,
2016)

Data-human mediations
• Argues for the importance of
technology itself, addressing themes
of design and infrastructure,
algorithmic affordances, and the
performativity of data

Future research
• Datafication of other aspects of life - from
banking to personal relationships and
workplace productivity - and their
relationship to health
• Data initiatives (including MyData)
addressing issues of data openness and
data ownership; asymmetries in terms of
data usage and distribution; the
inadequacy of current informed consent
and privacy protections; the need to
rearticulate concepts, such as sharing, or
public good.

Why do we need data activism?
Data activism can provide answers to how
capacities of data technology might be harnessed
to promote social justice, equality, new forms of
agency, political participation, and collective
action—and to challenge accepted norms and
ideological projects

Publications
Ruckenstein, M., Pantzar, M. 2015: Datafied Life: Techno-Anthropology as a Site for Exploration and
Experimentation. Techné: Research in Philosophy and Technology 19(2): 193–212.
Ruckenstein, M. 2015: Uncovering everyday rhythms and patters: food tracking and new forms of visibility and
temporality in health care. Techno-Anthropology in Health Informatics: Methodologies for Improving Human-
Technology Relations, 215, 28-40.
Janasik-Honkela, N., Ruckenstein, M. 2016: MyData: Teknologian orjuudesta digitaaliseen vastarintaan.
Tieteessä tapahtuu 34(2).
Pantzar, M, Ruckenstein, M., Mustonen, V. 2016: Social rhythms of the heart. Health Sociology Review.
Ruckenstein, M. 2016: Keeping data alive: talking DTC genetic testing. Information, Communication & Society.
Ruckenstein, M., Pantzar, M. 2017: Beyond the quantified self: Thematic exploration of a dataistic paradigm.
New Media & Society, 19(3), 401-418.
Ruckenstein, M., Schüll, ND. 2017: The datafication of health. Annual Review of Anthropology 46: 261-278.

Contact: Minna Ruckenstein
Consumer Society Research Centre, University of Helsinki
minna.ruckenstein@helsinki.fi

Individual perspective to MyData
Consumer attitudes towards MyData -based preventive
eHealth services
Timo Koivumäki
University of Oulu

• Large web-based
quesionnaire survey
• The objective was to
investigate what factors
influence consumers’
intentions to use a MyData-
based preventive eHealth
service prior to use
• The target population:
Finnish people of
working age (18–65)
Gender N %
Male 305 35.7
Female 550 64.3
Total 855 100
Age
18–25 352 41.2
26–35 227 26.5
36–45 119 13.9
46–55 107 12.5
56–65 48 5.6
66 and over 2 0.2
Total 855 100
eHealth services

• The survey constructs were measured with multiple, reflective items on a five-
point Likert scale
• Most of the measurement items were adapted from prior research to preserve
content validity. The only exception is the construct performance expectancy, in
which three additional items were included in order to reflect the expected
performance of the services under the study
• As the studied service did not yet exist, we had to measure habit with items
reflecting the use of existing eHealth/wellness technologies
eHealth services

• The most used/familiar
existing health
applications/technologies:
• Heart rate monitor
• Phone-based exercise app
eHealth services

KEY RESULTS
• The most valuable features of the services:
• The services educate one to affect his/her own health
• The services give feedback on how behavior affects health
• The services help the monitor own health
• The biggest barriers to service adoption:
• Concern related to the reliability of health information provided
• Lack of time to use the services
• Resistance to change the way of interacting with other people
• Uncertainty of whether the service would be effective
eHealth services

KEY RESULTS
• Factors affecting service adoption:
• Ease of use
• Self-efficacy related to technology use and healthy behavior
• Threat appraisals related to vulnerability and condition severity
• Almost 85% of the respondents would start to use MyData –base
preventive eHealth services if necessary
• In order to promote the adoption of preventive eHealth services
among consumers, it is essential to invest in increasing the general
awareness of healthy behavior and in the expertise of using
eHealth technologies
eHealth services

Contact: Timo Koivumäki
University of Oulu
timo.koivumaki@oulu.fi

Taking MyData into action
- Omaprofiili.fi
• Proof of concept for
MyData health
profiling service
• A web tool for citizen
to take control of their
health (/wellbeing)
data

Omaprofiili.fi – Features
• For other services to
use the individual’s
data with
authorization: Oauth2
+ REST API
• Complete download of
individual’s data from
the database
• GDPR compliance

Planned launch: late 2017
Demo version available at www.omaprofiili.fi
Video poster later today: come to talk to us!

Contact: Heidi Similä, heidi.simila@vtt.fi
Miikka Ermes, miikka.ermes@vtt.fi

riitta.sallinen@helsinki.fi1.8.2020
P4 medicine and DHR Pilot Study

How do we bring P4 (Predictive, Preventive, Personalized, and Participatory)
medicine and omics profiling into the current health care system?
Contrepois, K. et al. Clin Chem, 2016.Hood, Institute for Systems Biology, Seattle

1.8.2020 riitta.sallinen@helsinki.fi

DHR Pilot Study
Next-generation personalized health intervention: integrating
longitudinal deep multi-omics profiling, digital monitoring,
and personal data

DHR Pilot
Study
– Aims
2. How to collect, analyze and integrate
deep and comprehensive digital health
and wellness data?
3. Do personal health and wellness data,
coaching, and/or gadgets and apps
help to motivate and guide people to
make lifestyle changes?
4. How to return key actionable data to
people in order to coach individuals
towards better health?
5. Do lifestyle changes impact on
longitudinal data profiles?
6. What is normal variation in the
molecular profile of a normal adult
population?
Exploring principles
and practices of P4
medicine
1. Obtaining ethical approval

DHR Pilot Study
1.8.2020
Health
questionnaire
Health
check-up
Clinical
lab tests
Feces samples
 Metagenomics
or 16S rDNA
Quantified Self-
tracking:
Withings, Moves,
RescueTime
Digital
footprints:
S Group,
K Plus
Saliva samples
 cortisol
Study measures
Genomics
Proteomics
Metabolomics
Transcriptomics
Fitness tests
riitta.sallinen@helsinki.fi
• We recruited 107 healthy
volunteers (aged 25-59) among
the clientele of a private
occupational health care service
provider
• No previously diagnosed serious
diseases
• Individuals with elevated levels of
risk factors for lifestyle diseases
were encouraged to participate
0 mo 3.5 mo 7 mo 11 mo
Visit 1
Phase 1 Phase 2 Phase 3 Long-term
follow-up
3 yrs 5 yrs
Phase 4
14 mo
Visit 2 Visit 3 Visit 4 Visit 5

DHR Pilot Study – Phases
Biobanking of
samples (n≈20,000)
and data
 Data
feedback
 QS sensors
and apps
riitta.sallinen@helsinki.fi
1.8.2020
 Data
feedback
 QS
sensors
and apps
 Coaching
 Data
feedback
 QS sensors
and apps
 Coaching
 Social
networking

Study visit 1
Baseline
68% had one
or more of
these
BMI ≥25
37.1%
Blood pressure
≥130/85 mmHg
(or medication)
34%
Total
cholesterol /
HDL cholesterol
≥4 (or
medication)
12.4%
Vitamin D
<50 nmol/l
30.9%
Hemoglobin
<117 g/l (F) or
134 g/l (M)
2.1%
Fasting blood
glucose
≥6.1 mmol/l
8.2%
3. Do personal health and
wellness data, coaching,
and/or gadgets and apps help
to motivate and guide people
to make lifestyle changes?
N=97

68% 
57.8% had
one or more
of these
BMI ≥25
37.1%  33.7%
Blood pressure
≥130/85 mmHg
(or medication)
34%  33 %
Total cholesterol
/ HDL cholesterol
≥4 (or
medication)
12.4%  15.6%
Vitamin D
<50 nmol/l
30.9% 
15.6%
Hemoglobin
<117 g/l (F) or
134 g/l (M)
2.1%  3.3%
Fasting blood
glucose
≥6.1 mmol/l
8.2%  7.8%
Study visit 1
 5
N=97

How did the participants feel about the study?
• Most of the
participants
(86%) reported
improvement
in at least one
lifestyle factor
affecting
health and
wellness.
• The participants were highly enthusiastic about the study,
and 75.6% would participate again in a similar study.

Health Account – Terveystili
4. How to return key
actionable data to people in
order to coach individuals
towards better health?

Widén, E. & Ripatti, S. Duodecim, 2017.

Inherited risk for lactose intolerance

Vitamin D Compass

Coach App

“The ultimate goal is to have a
very detailed assessment of a
person’s physiological state so
that we can catch that state
when it goes aberrant.” Mike Snyder
iPOP
– integrated Personal Omics Profiling
• Personal omics profiling tries to bring together as many
measurements as possible to understand a healthy and disease
state in incredible detail.
Pook-Than, J. & Snyder, M. Chem Biol, 2013.

5. Do lifestyle changes impact
on longitudinal data profiles?
6. What is normal variation in
the molecular profile of a
normal adult population?

Next step:
Integrating longitudinal deep multi-omics
profiling, digital monitoring, and personal data
Price et al. Nat Biotechn, 2017.

DHR Consortium
Maritta Perälä-Heape, University of Oulu
Harri Honko, TTY
Antti Kallonen, TTY
Sari Yli-Kauhaluoma, KTK
Minna Ruckenstein, KTK
Nina Honkela, KTK
Kirsi Halttu, University of Oulu
Virve Inget, University of Oulu
Saara Pekkarinen, University of Oulu
Juho Rantakari, University of Oulu
Miikka Ermes, VTT
Marja Harjumaa, VTT
Mikko Lindholm, VTT
Heidi Similä, VTT
FIMM
Olli Kallioniemi
Riitta Sallinen
Myles Byrne
Kati Donner
Pyry Helkkula
Iiro Hietamäki
Ilkka Jokinen
Mari Kaunisto
Kaisa Kettunen
Anu Karhu
Päivi Lahermo
Hannele Laivuori
Timo Miettinen
Robert Mills
Teemu Perheentupa
Samuli Ripatti
Janna Saarela
Ida Surakka
Paula Vartiainen
Heidi Virtanen
Elisabeth Widén
Others
Kristina Hotakainen,
Mehiläinen
Sirpa Soini, Helsinki Biobank
Heli Viljakainen, University of
Helsinki & Folkhälsan Research
Centre
Kiitos!
SciLifeLab (Stockholm)
Lars Engstrand
Thomas Moritz
Peter Nilsson
Maja Neiman
Jochen Schwenk
Valtteri Wirta
Uppsala Universitet
Ulf Gyllensten
Stefan Enroth

Video posters and demos
• Making MyData approachable - user interfaces
• Sandbox demo
• The datafication of health - key insights and future research
• MyData health profiling tool - omaprofiili.fi
• Revenue models - MyData operator view
• Business models - case health insurance companies
• Consumer attitudes towards MyData -based preventive eHealth services
• DHR pilot

Digital Revolution in Healthcare System

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