Using Big Data to take Guesswork out of Disaster Managment

1. WP 7:
Lunch and Learn
Using ‘Big Data’ to Take the Guesswork
out of Disaster Management
Winnipeg, MB
October 19, 2017

2. 3
WP7 TEAM LEADS
7.1 Paul Stolee, Don Juzwishin
Policy and Regulatory Issues in
Enabling Technological Innovation
(PRI-TECH)
7.2 Josephine McMurray, Heidi
Sveistrup
Developing Regional Health
InnoVation Ecosystems (DRiVE)
7.3 Joon Lee, John Hirdes
Data-driven decision-making in
Health care (3DHC)

3. 4
What you’ll understand better after this workshop
1. Technologies are broadly defined i.e. machine
learning/AI, virtual reality, social media
2. How “big data” from a variety of sources can be
transformed by technologies, and used in
innovative ways
3. Health systems present complex challenges to
adopting innovative technologies
4. Improving the lives of vulnerable populations
requires a multi-sectoral, multi-disciplinary
approach in addition to supportive infrastructure,
processes, and funding

4. 5
How we’ll spend our time together today...
1. Definitions...just so we’re speaking the same
language!
2. Why it takes a “village” [ecosystem] to raise a
health innovation
3. The challenges faced by new health technologies
4. Case analysis: Big Data & the VPR

5. 6
A shared understanding of terms...
Technology:
Previously…
“The substitution of equipment for human labor” ( Balue et al.,1976, p. 21)
Now…
“The application of scientific knowledge for practical purposes, especially in
industry” (Oxford English Dictionary, 2017)

6. 7
A shared understanding of terms...
Innovation:
"a process through which economic or social value is extracted from
knowledge—by creating, diffusing, and transforming ideas—to
produce new or improved products, services, and processes."

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8. 9
Project 7.2
Understanding how regional ecosystems can contribute
to the creation of a fertile environment for health and
ageing technology innovation (from conceptualization to
commercialization)

9. 10
Project 7.2
Transforming regional ecosystems through collaborative
processes

10. 11
Project 7.1 PRI-TECH
Understanding and addressing multiple barriers and
disincentives which limit the adoption and health
benefits of technologies…

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In their words...
MESSY SPACE: “Home and community care…you’re
in the messiest space of all for technology
adoption. This sector is significantly underfunded in
every way shape and form, significantly under-
resourced, massively complicated…just a nightmare
to organize”
TYRANNY OF THE ACUTE: “It’s extremely hard for
those procurement agencies to think about adoption in
the home and community care sector because …the
tyranny of the acute prevail. So the highest cost
expenditures are still the hips and knees. The
most influential interests are still the acute care
hospitals, the doctors and the surgeons.”
VALUE OF ECO-SYSTEMS: “ you have to create a
community, an innovation community, where you
have the researchers, you have potential industry
partners...SMEs might be in there, big companies.
You have users , you have clinicians, you have health
service providers, and you have policy makers. If you
want to it to be working, let’s have it run by the policy-
minded”
CLINICAL DOLLARS NOT FOR TECH: “the
medical device sector has some pretty big money in
it …and the only way that Canadian companies will
be able to position themselves and capitalize on
that gross market is if they can access the clinical
and patient populations…in Canada, this is a
major issue, because no clinical dollars… or
budgetary dollars that are for the delivery of
health care can be used for the things like
prototype testing or product development or
product design”
ARTICULATING A BUSINESS MODEL : “What
really hit this spinoff very hard was that they could
not articulate a business model.. it was really
hard for them, they, they had a great team they
really worked closely with clinicians and, and the
whole idea was to reduce unnecessary
hospitalization and, emergency room visits. At the
same time, they really struggled to find who’s
going to pay for this system”

12. 13
Project 7.3
“Using “Big Data” to Take the Guesswork out of
Disaster Management”

13. 14

14. 15
interRAI
•Not-for-profit international collaborative
•Involves clinicians and researchers from over
30 countries
•Promotes evidence-informed clinical practice
and policy decision making
Improving the quality of life of vulnerable
persons through a seamless
comprehensive assessment system.

15. 16
Home Care assessment mandated in 7 provinces and
one territory
• Long Term Care assessment
mandated in 8 provinces, and 1
territory, piloting in QC and NWT
• Long-stay home care clients are
assessed on admission and then
routinely for the rest of their stay
(reassessment interval varies by
jurisdiction)

16. 17
Development of the VPR
•Alexandra Van Solm, PhD used home care data to
develop an algorithm using Ontario HC data
•Compared it to Case Manager ratings of Emergency
Response Level
•Tested it using data collected routinely during the
Ontario ice storm
Higher risk individuals in the affected area were more
likely to die or be admitted to long-term care than those
who were also at high risk but lived outside of the
affected area.

17. 18
Some Region of Waterloo facts...
Location: Southwestern Ontario, CAN
Cities: K-W, Cambridge, rural towns
Population: 559,000 (2017)
Education: 2 universities, 2 college
Research: 150 research institutes
Patents per capita: 4 x’s Cdn average

18. 19
Time for some group brainstorming!

19. 20
Read the case & discuss the following questions
1. What sectors and groups might benefit from this innovative technology
(the algorithm and VPR)?
2. Think about the gaps this technology (the algorithm) might fill for those
groups. What is the value this technology offers to the various groups i.e.
what “pain points” in the current system will your technology be fixing,
what are the costs etc?
3. What additional work might be needed on the technology for it to be a
proven solution for use?
4. At this time, what stakeholders should be included in your project? What
should their role be? Are there others you should add over time?
5. How will you and this group move the project forward? What barriers do
you anticipate to its adoption and how might you overcome those barriers?

20. 21
Group Feedback

21. 22
What actually happened...
• Links between public health and emergency
management departments in Waterloo-Wellington
have allowed for exploration of the potential of the
VPR using GIS mapping technology
• The following slides show maps where the density of
those with high VPR scores in a postal code region is
overlaid with flood plain information

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25. 26
Waterloo Wellington LHIN distribution of VPR

26. 27
Wrap up
1. “Big data” is being collected manually i.e.
interRAI, and automatically i.e. sensors,
smartphones
2. Think “out of the box” about how to combine
technologies with data i.e. bed sensor data with
wearable
3. “Wicked problems” require multi-sectoral,
multi-disciplinary solutions
4. Stakeholder collaboration is the first step
identifying and addressing barriers

27. THANK YOU
stolee@uwaterloo.ca
https://uwaterloo.ca/geriatric-health-systems-research-group/
stolee@uwaterloo.ca

28. Welcome
to Our Presentation
Birmingham, August 25, 2013