Analyzing Digital Health Solutions

1. Afik Gal, 2014
The newest version of this document can be found at
http://severalthingscometomind.com

2. 1. Category definition
2. Sources of value (SOV) matrix
3. Behavior change capabilities matrix
4. Fit with conventional medicine
5. Business Model
6. Adoption
7. Potential revenue streams

3. Useful:
• By disease (e.g. diabetes, mental health)
• By use case(e.g. disease management, acute care, prevention, wellness)
• By task
– Guidance + Monitoring +Feedback (disease management)
– Administrative (e.g. scheduling, care coordination)
– Screening/Diagnostics
– Education
– Support (e.g. incentives, coaching)
– Accessibility, cost reduction
– Adherence/Compliance
Less useful:
• Form factor (e.g. wrist, wearables, smartphone)
• Modality (e.g. device, app, sensor, mobile phone, combinations)
• Buyer (e.g. consumer, payer, provider)

4. http://mhealthwatch.com/strategy-analytics-
nike-still-dominates-mobile-health-apps-23085
WellDoc AliveCor MC10 WellFrame FitBit MDRevolution
BiologicalPhysical
Hardware + +++ 0
Software ++ + 0 0
Services
Experience 0/+ +
Process 0
Algo-Recipe ++
Algo-Transform + X 0
Algo-Discover +? +? 0
Algo-Solve +?
Integration +
TradeCollaboration
Development ++
Social 0
Data ? ? ? + +
Behavior change 0 0 0 +
Table shows multiple examples that are not compared against one another. Scoring is based on comparison with comparable companies.

5. Behavior change is a science and its importance in digital health is often undervalued.
The following capabilities are required to facilitate behavior change:
• Triggering –identification of the right time and context to interact with the user
• Personalization – messages are personalized according to user’s needs and context
• Feedback loop – a feedback loops around the target behavior is created
• Motivation psychology – utilization of incentives, gamification, messaging, behavior
economy and other to increase intrinsic and extrinsic motivation
• Focus on habit formation – focus on long term maintenance of the behavior
• Continuous improvement – the behavior change intervention is constantly tested and
improved based on data collection and analysis (e.g. A/B testing)
WellDoc Pact OMSignal FitBit MDRevolution
Triggering + ++ 0 0
Personalization + + ++
Feedback loop ? 0 + 0 +
Motivation psychology ? ++ ++ 0
Focus on habit formation 0 0 0
Continuous improvement 0 +

6. • HCPs engagement
– Workflows - improvementadditionremoval vs.
amount of value added
– Incentives – financial? Reimbursement/other? vs. extra
efforts needed
• Legal/regulatory
• Patients
– DesignUser experience
– Agediseaselifestyle limitations
http://medcitynews.com/2014/05/mobile-health-companies-need-make-technology-clinically-
relevant/

7. • Solution value proposition: set of benefits for each type
of buyeruser (comparison is easier using a taxonomy
such as Osterwalder VP Canvas)
• Traction >> Evidence ≥ Perception
• Friction required to get to the market
– Depends on buyer/user and the use case
– Risks: execution (time, money), differentiation, early
mover advantage
– Addl. barriers when targeting HCPs- FDA approval,
clinical trials, CEREBM, HCPs education

8. • Digital health is mostly in the ‘Early adopters’ phase
• Wellness appsactivity trackers and Telemedicine are a
bit further ahead on the adoption curve
• There are separate adoption curves by stakeholder (e.g.
consumers, providers, payers, care givers) and separate
curves by the use case
• Ecosystem influences adoption!
– Joint creation business model
– Platform?, APIs?
– Role of solution in ecosystem
and its resilience to drastic
changes in it

9. • Sale (fixed fee ± recurrent consumables)
• Licensing (fee/time unit)
• Subscription (fee per user/time unit)
• Utilization (fee/consumption unit)
– Requires platform/APIs approach – usually later stage
• Rake (% of transaction)
– Requires active marketplace and platform – risky
• Professional services (fee/hour)
– Integration/installation/maintenance fees are less
common with cloud based solution
– Need for PS, can slow down adoption (e.g. Trialability)

10. Health
Watch hWear
iRhythm ZIO
XT patch
AliveCor
Sotera
Visi
QardioCore
Use cases Diagnostics,DM Diagnostics,DM DM DM DM,Sports
Evidence ++ + +
Fit with conventional
medicine
Additional?
Incentives?
++ ? ? N/A
Role Standalone Standalone Standalone Standalone Standalone
Hardware ++ + + + +?
Experience + +
Algo-Transform
Algo-Discover ++? ++?
Development
Social +
Data ++ ++ ?
Triggering
Personalization
Effective feedback loop
Motivation psychology
Assessment based on WWW and media data – might be inaccurate

11. MC10 Numetrex OMSignal Sensoria
Use cases Tech
Platform,DM
Sport Sport Sport, DM
Evidence ? N/A N/A N/A
Fit with conventional
medicine
? N/A N/A N/A
Role Platform Standalone Standalone Standalone
Hardware +++ + + +
Experience + + +
Algo-Transform +
Algo-Discover + + +
Development +
Social +
Data + +
Triggering +++ + ++
Personalization ? ++ ?
Effective feedback loop ? ++ ?
Motivation psychology ? ++ ?
Assessment based on WWW and media data – might be inaccurate