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Analyzing Digital Health Solutions

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Analyzing Digital Health Solutions

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Analyzing Digital Health Solutions

  1. 1. Afik Gal, 2014 The newest version of this document can be found at http://severalthingscometomind.com
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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