mobcon dh 2015 - ryan sysko - mobile prescription therapy a scientific approach to digital health
TRANSCRIPT
MOBILE PRESCRIPTION THERAPY: A SCIENTIFIC APPROACH TO DIGITAL HEALTH
Ryan SyskoWellDocChairman & Founder
Personal Use/Data Sharing
Treatment/Coaching
WellnessChronic Disease
Management
Digital Health Landscape
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“Rigorous evaluation of e- & m-Health is necessary to generate useful evidence and
promote the appropriate integration of technologies to
improve health and reduce inequalities.”
Source: PubMed Database, GSMA Literature Review of State of Evidence on mHealth 2011Slide created by Alain B. Labrique, PhD, MHS, MS; Associate Professor; JHU mHealth Initiative
The Bellagio eHealth Evaluation Declaration 2011
5Source: PubMed Database, GSMA Literature Review of State of Evidence on mHealth 2011
Slide created by Alain B. Labrique, PhD, MHS, MS; Associate Professor; JHU mHealth Initiative
The Bellagio eHealth Evaluation Declaration 2011
If used improperly, eHealth may divert valuable resources and even cause harm…
implementation must be guided by evidence…
6Source: PubMed Database, GSMA Literature Review of State of Evidence on mHealth 2011Slide created by Alain B. Labrique, PhD, MHS, MS; Associate Professor; JHU mHealth Initiative
State of Evidence in mHealth
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Am
ou
nt
of
Info
rmat
ion
(R
ED)
Threshold of “Information”
Stability Functionality Useability Efficacy EffectivenessOF WHAT ?
Systems Engineering Qualitative Quantitative Mixed Q/Q / M&E
Slide created by Alain B. Labrique, PhD, MHS, MS; Associate Professor; JHU mHealth Initiative
State of Evidence in mHealth
50,000+ digital health products
1,000+ apps for diabetes
FDA Cleared <0.1% mobile medical applications
A Closer Look at Diabetes…
Published clinical data
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mHealth Evidence Repository
Slide created by Alain B. Labrique, PhD, MHS, MS; Associate Professor; JHU mHealth Initiative
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Why Isn’t the Evidence (particularly RCTs) More
Robust?
Randomized Controlled Trials (RCTs) Require:
• Collaboration
• Expertise
• Funding
• Resources
• Time
• Positive Outcomes
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Why Do We Need Evidence?
Slide created by Alain B. Labrique, PhD, MHS, MS; Associate Professor; JHU mHealth Initiative
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Payers: Clinical Evidence Required for Coverage
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Drug, Study Design Medical Condition
Metformin RCT Non-insulin-dependent diabetes
De Fronzo et al, 1995* mellitus poorly controlled
with diet or sulfonylurea
Metformin vs placebo: (glycemic) 189 vs 244
A1C 7.1 vs 8.6, P < .001
Metformin + glyburide vs glyburide: (glycemic) 187 vs 261
A1C 7.1 vs 8.7, P < .001
Results
75% increase
204% increase
Providers: Power of Evidence (and Marketing)
RCT Published
*De Fronzo RA, Goodman AM. Efficacy of metformin in patients with non-insulin dependent diabetes mellitus. The Multicenter Metformin Study Group. N Engl J Med 1995;333;451-9
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Survey of how patients want to learn about
Mobile Medical Applications
Rx61%
OTC20%
Either19%
Patients: Still Trust Their Doctors
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Patient Guidance
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Smart Visit Report Population Data Reporting
Clinical Decision Support
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First Technical Data
Reasonable & Necessary
Proven Clinical Effectiveness
Safety & Efficacy
Initial Market Presence Broad Market PresenceDiscovery FDA
RCT #1 (Quinn 2008)
Human Factors Testing
Class II Submission
RCT #2(Quinn 2011)
Demonstration Projects
EMR Integration
Coverage Standard
Clinical Care Standard
BlueStar
Evidence Development Roadmap for BlueStar
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Study Population• Privately insured adults with type 2 diabetes under age 65
• Patients cared for by primary care providers (PCPs) in local communities
• Physician practices (n=26) randomized to control or intervention
• Eligible Patients, HbA1c > 7.5%, enrolled based on physician’s study assignment
Primary Aim• To compare the 3, 6 and 12 month changes in A1c among patients with diabetes assigned to the
WellDoc solution compared to A1c changes among patients with diabetes who are assigned to usual care
Secondary Aims• Healthcare utilization
• Patient usability
• Patient adherence
• Provider prescribing behavior
BlueStar Pivotal Study
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Patient Coach &
Provider Decision Support
Control Group Intervention Group
• Automated, real-time and
longitudinal patient coaching
• Analyzed patient data
interpreted using evidenced
based guidelines
• No Change in Care
Usual Care
Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. A Cluster Randomized Trial of a Mobile Phone
Personalized Behavioral Intervention for Blood Glucose Control. Diabetes Care September 2011vol. 34 no. 9 1934-1942.
BlueStar Pivotal Study
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Mobile Diabetes Management Study (n=163 )
Age 52 years + 8.1
Sex Male 49.7%
Female 50.3%
Race African American 39.3%
Caucasian 52.8%
Other 13%
Education High school or less, 30.1%
Some college, 38.7%
Bachelors Degree or higher, 31.3%
Years with
DM
8.2 +5.9 years
Smoking
Status
Current Smokers – 17.2%
Former Smokers – 6.7%
Non-Smokers – 76.1%
BMI 35.4 kg/m2 +7.4
Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. A Cluster Randomized Trial of a Mobile Phone
Personalized Behavioral Intervention for Blood Glucose Control. Diabetes Care September 2011vol. 34 no. 9 1934-1942.
BlueStar Pivotal Study
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• The main research outcome was mean change in A1c from baseline to 12 months.
• We found a statistically significant 1.9% mean decrease in A1c for the intervention group
compared to a 0.7% mean decrease for the control group.
• These results are similar to our findings in the previously reported pilot study.
Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. A Cluster Randomized Trial of a Mobile Phone
Personalized Behavioral Intervention for Blood Glucose Control. Diabetes Care September 2011vol. 34 no. 9 1934-1942.
BlueStar Pivotal Study
Virtual, remote, or face-to-face. Product support for
patients & providers.
Face-to-face physician detailing.
Provider In-Servicing Customer CarePatient Training
Sales & Marketing Model
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Product Mobile & Website w/ SEO
Virtual Training Menu
Lobby Sample BS Day Invitation Self ID Poster
Awareness Poster
Physician-to-Patient
Talking Points
Traditional Professional & Consumer Marketing
Marketing Tactics
Gaining Rx Traction
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2Q14 3Q14 4Q14 1Q15
BlueStar Quarterly NRx Growth
Hired Initial Sales Force in 2Q14
Strong Initial Adoption by Providers
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2Q14 3Q14 4Q14 1Q15
BlueStar Cumulative Prescribers55% of targeted
prescribers wrote an Rx
The average A1C reduction is .85 across all patients. For patients with beginning A1C values of greater than 8, the average A1C reduction is 1.38.
Regional Payer Pilot Clinical Results
Group nAvg Starting
A1C
Avg 2nd A1C
Reading
Avg Reduction
in A1C
All Patients 99 8.72 7.87 (0.85)
Starting A1C < 7.0 17 6.58 6.63 0.05
Starting A1C b/w 7.0 & 8.0 26 7.42 7.15 (0.27)
Starting A1C > 8 56 9.97 8.59 (1.38)
Group nAvg Starting
A1C
Avg 2nd A1C
Reading
Avg Reduction
in A1C
All Patients 99 8.72 7.87 (0.85)
Starting A1C < 7.0 17 6.58 6.63 0.05
Starting A1C b/w 7.0 & 8.0 26 7.42 7.15 (0.27)
Starting A1C > 8 56 9.97 8.59 (1.38)
MEAN 8.6 A1C
76%
24%
The Product is Being Prescribed Appropriately
…and for Patients Across Age and Gender
8% 21% 38% 33%
44%
Female56%Male
67% 33%
Used Across Technology Platforms
Prescribed Across Therapies & Used Similarly
45%
55%
55%
45%
Type 2 Patient Testimonies
“I’m not alone anymore”
“I was dx 12 yrs ago, Where
has this been?”
“my doctor finally
understands me”
“Helps me connect the dots…
never knew oral health could
impact my diabetes”
“It’s like a diabetes
class always with
me”
“I’m now trying after
meal testing so I can
get the feedback”
“This is my 1st
Smartphone, but
I can do this!”
“a 20-year
weight liftedoff my back”
Questions
Discussion and Q&A