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Diffusion H4R Stanford 2020

Diffusion Hacking for Recovery Stanford 2020

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Diffusion H4R Stanford 2020

  1. Team Diffusion Michael Fanton ME PhD Product/R&D Mike Snyder BioE MS, MBA Business/Strategy August Domel PhD, Postdoc Strategy/Ops/R&D Jack Keene MD, Emergency Med. Med/Admin/Strategy/Risk Matthew Hernandez ME BS, MS&E MS Ops/Strategy Prevention and mitigation of head and neck injuries resulting from falls Better detection and prevention in extreme sports Fall prevention through wearable sensors Mentors: Joy Fairbanks and Pradeep Jotwani
  2. 48 interviews to date (11 since day 4) 11 Contact sports experts Nursing home administrators Nurses Doctors Senior family/guardians 9 164 5 Patients 1 Construction safety managers 2
  3. Day 1: Business model canvas Key Partners Key Activities Key Resources Value Propositions Customer Relationships Customer Segments Channels Cost Structure Revenue Streams - Engineering design firms - Manufacturing facilities - Safety standard groups / regulatory bodies - Sports leagues (universities, professional, etc.) - Hospitals - Nursing homes - Increased safety and reduction in injuries - Reduction in liability for helmet manufacturers - Increased marketability - Improved helmet standards - Helmet manufacturers of all kinds (football, bike, motorcycle, military, construction, etc.) - Direct to consumer for sensor tech - Sport teams - Nursing homes - Hospitals - Directly to elderly in home - Setting up key partnerships - Tech development of hardware and software - Manufacturing - Marketing key benefits of tech / scientific breakthroughs - Dedicated sales reps - Continued automated service via analyzed sensor data - Retail stores, including sporting good stores - Direct sales to leagues/teams/schools - Existing channels via key partners - Athletic trainers - Hospitals Iteration #1 - Intellectual property - Quality assurance - Capital for manufacturing and testing - Partnerships and agreements - Sales and marketing - R&D and testing - Data storage and upkeep - Sales and marketing - Any potential licensing revenue stream - Data subscriptions and usage (consumer and businesses)
  4. Day 1: What we thought People want safer helmets
  5. Day 1: What we learned Extreme Sports ● Safety does matter to users but not aware of which brands are safest ● Competitive, crowded market - hard to break into due to established relationships ● Limited market size Construction ● Construction managers buy hard hats ● Helmet performance does not matter to them - all about compliance and meeting regulations ● No demand here ● Aesthetics (looking cool is better) Elderly falls ● Elderly falls a huge problem both at home and in care facilities ● A lot of technologies invested in fall protection but still a big problem… why?
  6. Days 2 - 4 - Value Proposition Better fall detection is needed to decrease response time and provide peace of mind Long-term activity monitoring to identify patterns in mobilityBetter fall prevention is needed - intervene earlier in fall sequence Acute detection of “risky” situations Interviews with nurse administrators, elderly guardians Interviews with geriatricians, elderly guardians Interviews with nurses and nursing home administrators
  7. Days 2 - 4 - Customer segments Family/caregivers Nursing Homes Who: An adult with senior parent or family member living alone Motivation: Want to prevent injuries and optimize health for their loved one Pain point: Current life alert systems detect falls after they have happened. The technology doesn’t prevent. Who: Long-term care and short- term rehabilitation facilities for elderly population Motivation: Care about preventing falls, are penalized (state rating lowered, or even closed) for falls that become injury. Can be fined. Pain point: Alarms/monitors are available but are insufficient to meet needs and do not prevent falls
  8. Day 4 MVP - We PREVENT falls in care homes 1. A detachable MEMS-based sensor package (accelerometer, gyroscope) 2. Adhesive patch holder to attach to skin 3. Monitor system for clinician or nurse to see activity of all patients 4. Send alarm of impending fall to staff when risky behaviors are sensed by at- risk patients (e.g. sitting up in bed at night) Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Activity detection MEMS sensor package Clinician monitor
  9. MVP Revenue Model Subscription cost per patient per month Diffusion ● Leased hardware ● Disposable adhesives ● Data analytics Data to improve algorithms/IP Nursing homes / assisted living Insurance reimbursement?
  10. Final business model canvas Key Partners Key Activities Key Resources Value Propositions Customer Relationships Customer Segments Channels Cost Structure Revenue Streams - Engineering design firms - Manufacturing facilities - Doctor referrals - Nursing homes - Accountable care organization - Insurance Nursing Homes: - Acute fall prevention - Fall injury mitigation through activity monitoring Direct to consumer - Elderly activity monitoring for health optimization - Fall prevention - Fall detection - Assisted living/homes - Senior guardians - Setting up key partnerships - Tech development / validation of hardware and software - Manufacturing - Marketing key benefits of tech / scientific breakthroughs - Pilot studies - Dedicated sales reps (nursing homes) - Online (D2C) - Continued automated service via analyzed sensor data - Online sales (D2C) - Direct sales for nursing homes - Existing channels via key partners - Intellectual property - Quality assurance - Capital for manufacturing and testing - Partnerships and agreements - Sales and marketing - R&D and testing - Data storage and upkeep - Sales and marketing - Clinical trial costs - Data subscriptions and usage (consumer and businesses) - Disposable and durable good - Insurance reimbursement for nursing homes?
  11. Day 5 - Where we are now Interviews: 2 nurses, 1 sensorimotor neurophysiology expert, 7 family caregivers, 1 geriatrician (11 total) Senior activity monitoring to track health and optimize wellness outside of nursing homes remains VERY intriguing to caregivers and geriatricians. Could be integrated into existing products (e.g. glasses or hearing aids)
  12. Day 5 - US Market size ○ Assuming $75/mo per patient ○ Frail and prerail elderly markets do not include nursing homes ○ Total elderly population projected to grow by 45.9% by 2040 Nursing Homes 1.3 M patients $1.17 B Frail Elderly (Prefrail Elderly) ~ 8.629 M Americans (25.6 M) $7.77 B ($23.1 B) Residential Care Communities 812 K residents $731 M
  13. Day 5 - What’s next? Q1 2021 Establish partnerships to begin pilot study Q4 2020 Small seed funding, incubators 8/2020 Continue to test business hypotheses / iterate (more customer discovery!) 9/2020 Assemble team, create initial prototype

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Diffusion Hacking for Recovery Stanford 2020

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