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