2. Healthcare is not serving
the patient...
I’m not sure what to recommend for OK, I’ll give it a try but I’ve
your increased pain... maybe you already tried so many
can try this new drug? different meds!
3. Big data... big deal
Power of secondary data lost if not used to change healthcare delivery
0 01
10
1 11 0 1 1 1 0 1
1 1
0 1 1
00 0 1 0
1
0 1
1
0 1 0
0
0 0 1 0
0 0 1
1 1
0
1
10
1 1 1 0 1 0 0 0
1
0
0 1
0 0
0
0 1 10 10 0 01
0
1
1 1 0
1 0 0
0 0
1 0 1
1
0 1
0
1
4. How can patient data be useful?
Ubiqi provides a structured experiment framework
DISEASE TRACKER PERSONALIZED
FEEDBACK
PASSIVE MOBILE/
DEVICE DATA
CLINICAL
EFFECTIVENESS
3RD PARTY DATA
Personal Discovery
Engine
ITERATE
5. We power “citizen science”
through n=1 experiments
A B BA
AA B B
BASELINE SAMPLE HYPOTHESES RANDOMIZATION OUTCOME
AGGREGATION
PATIENT: HEALTHCARE ORGANIZATIONS:
Identify triggers Understand patient behavior
Understand symptoms Analyze patient response
Compare therapies Compare therapies
Report side-effects Review safety data
6. Apply same framework across
multiple conditions
pain respiratory gastrointestinal
7. We create value for many
healthcare organizations
HEALTHCARE
PHARMA
IT (EMR)
PATIENT
PHARMACY
PHARMACY DISEASE
BENEFITS PROVIDERS
CHAINS MGMT
MGMT
PAYER
9. Addressable market is 63M
people in US alone
250M
AMERICANS COVERED BY HEALTH INSURANCE
125M
HAVE CHRONIC CONDITION
63M
BENEFIT FROM
SELF-MANAGING
OUR ADDRESSABLE MARKET
10. Ubiqi has a unique approach
ACTIONABLE INSIGHTS
UBIQI
GENOMERA
PROVIDER-DRIVEN PATIENT-DRIVEN
PROTOCOLS PROTOCOLS
STUDY
CURE
THE
RINGFUL
CARROT
HEALTH
CONDITION TRACKING
11. 30+ years of experience
in healthcare, technology,
and design
ADVISORS
ESTHER DYSON
Jacqueline Thong, CEO JAYANT PARTHASARATHY, PHD
CHRIS ADAMS, MBA, PHD
Anshuman Sharma, CTO EGILIUS SPIERINGS, MD, PHD
William Tang, Design Lead MATTHEW MAMET
12. We have strong results
Partnerships
From initial migraine minimum viable product:
18K+ patients
20% active for more than 6 months
Positive health outcomes
!
Paying customer
Partnerships
In discussion
13. Go-to-market approach
REPLICATE
MODEL FOR
BACK PAIN
REFINE
PRODUCT FOR SCALE PLATFORM
MIGRAINE - Prove utility beyond migraine
- Gather data
- Validate payer revenue model
REFINE PLATFORM
MIGRAINE - Prove clinical validity
MVP - Gather data
- Scale pharma model
- Evaluate payer interest
MINIMUM VIABLE PRODUCT
- Prove utility to patients
- Gather data
- Evaluate pharma interest
- Validate pharma revenue model
14. Payers are our key target;
first build credibility
2013 2014 2015 2016 2017
REVENUE $600K $4M $16M $45M $75M
PHARMA PILOTS
SCALE PHARMA MODEL
PAYER PILOTS
SCALE PAYER MODEL
15. Seeking guidance in
partnership development
and $700K 3%
3%
KEY USE OF FUNDS
FOR Q12013 - Q42013
9%
R&D
10%
Business development
COGS 55%
Admin
B2B Marketing 20%
User acquisition
16. We envision acquisition
by a payer or disease
management company
Disease management companies
Healthcare payers
17. Help us create this
alternative future!
I saw that when I do yoga AND take That’s great! Let’s
the new meds, my daily pain scores work together to make
are 20% lower! that even better.
18. Get insights. Better health.
Jacqueline Thong
co-founder / CEO
jacqueline@ubiqihealth.com
+1 617 794 2089
www.ubiqihealth.com
20. ROI to pharma
Example: Eli Lilly / Boehringer-Ingelheim’s Tradjenta
Revenues are $3.5B annually (2.3M pts)
Adherence is 50% for oral anti-diabetic medications
Estimated $1.75B revenue lost per year
250K patients enroll in Ubiqi
At $5 per patient per month
B-I PAYS UBIQI $1.25M (1 year)
10% of users get better results
25K patients increase adherence to 75%
B-I INCREASES REVENUES BY: +$47M (1 year)
21. ROI to payers
Example: Aetna has 16 million subscribers
8% have asthma, costs them $2500/pt
25% enroll in Ubiqi program: 320K subscribers
At $1 per member per month...
AETNA PAYS UBIQI $3.8M (1 year)
25% of users get better results
80K subscribers reduce ER visits, hospital visits, unnecessary
prescription meds, save $500 each/yr
AETNA SAVES: $40M (1 year)
22. Intellectual Property
Items we anticipate constitute defensible IP:
i) process of taking structured / unstructured data and extracting
feature sets that are disease-specific and map to clinical evidence;
ii) learning engine which allows user to construct personalized
experiments that they can run to create evidence;
iii) algorithm that makes suggestions on what pieces of an
experiment the user might want to choose to ensure success based
on base-line and crowd-sourced data;
iv) algorithm that aims to increase the information contained in
unstructured patient-reported data to have stronger mapping
between evidence and outcomes
