Discover more from Pear Healthcare Playbook
Lessons from Pear VC: What is in a good healthcare AI startup?
Everything from exciting opportunities AI x Healthcare, solving GTM incentives into healthcare systems, and what is in a good healthcare AI startup?
Welcome back to the Pear Healthcare Playbook! Every week, we’ll be getting to know trailblazing healthcare leaders and dive into building a digital health business from 0 to 1.
*To keep with theme, I posted my photo with the cover photo. Feels funny!
Today we're excited to dive into what we believe is in a good healthcare AI startup — everything from how to sell into payers, providers, pharma and consumers to identifying exciting opportunities in AI and Healthcare, building a defensible and successful long term AI and healthcare startup.
I was a guest for a Stanford course on Artificial Intelligence in Medicine and Healthcare Ventures and got a few requests to share the content. I decided to take some time to add a few anecdotes from some of the brightest minds in AI, healthcare, and entrepreneurship, especially around go-to-market, because technology is only a big business opportunity if healthcare organizations are willing to adopt and change.
Opportunities in AI in healthcare
AI in Non-Clinical Workflows Market Map
AI in Bio and Pharma Market Map
AI in Clinical Workflows and Patient Tools Market Map
Every startup has their own unique journey in the 0 to 1 journey to Product Market Fit and healthcare organizations are all unique and have different characteristics but this is meant to help generalize a few patterns into a playbook to help entrepreneurs and innovators navigate the complex ecosystem and ultimately build better patient outcomes. I really hope this can help you get to speed quickly on how to build a great healthcare AI startup and considerations on GTM and also defensibility and ultimately, help you build a category defining company that will drive better patient outcomes and lower costs!
In this presentation, we cover:
(0:00) Background and Pear healthcare
(1:51) Pre-seed fundamentals:
Multidisciplinary team and insight
Hair on fire problem
Why now for a 10x product
(2:56) Tailwinds for AI and Healthcare
Healthcare spend is rapidly growing at 7.1% CAGR at 20% of US GDP ($4 Trillion)
Healthcare funding is down since 2021 but still steadily growing over last decade
LLM innovation and infrastructure, including specialized models in Healthcare
Exponential growth of FDA approval in medical AI algorithms (896+ in the last five years cleared by FDA)
Regulation driving opportunities for price transparency and interoperability
Physician and clinician burnout at all time high
(5:00) Figuring out incentives in Healthcare and who is willing to pay
Breaking down each buyer into: Incentives, Decision maker, Decision speed, Business models
SMB Provider Clinics have fewer resources so will invest in productivity tools and have less bureaucratic sales cycles
Hospital systems are notoriously hard to sell into, due to thin margins and more sensitive to integration and clinical workflow changes
Medicare and Medicaid payers are more value aligned in improving care and reducing costs in the system, though they need a lot of ROI to implement new technologies
Employers are overwhelmed by startups pitching benefits and cost reductions, but will pay for top 5 disease burdens by cost
Pharma companies are willing to pay for technologies that improve R&D and Sales and Marketing, and have many stakeholders with different needs
Consumers are the center of the healthcare universe but if they’re paying out of pocket, it is difficult to acquire consumers directly
(12:34) Opportunities in AI in healthcare
(14:10) Pear AI thesis: specialized models fine-tuned for application workflows powered by proprietary data, healthcare AI readiness, ethical and safety considerations
(16:52) Developing a moat in AI Healthcare
(17:39) PMF is not enough, need a big market
(18:41) Funding milestones
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Special thank you to all those contributed to this knowledge piece with their insights, perspectives and input.
Mar Hershenson, Aparna Sinha, Arash Afrakhteh, Addison Leong, Liz Burstein, Abhishek Chandra, Sachin Jain, Alexandre Lebrun, Vivek Natarajan, Viswesh Krishna, Jimmy Qian, Kevin Wu, Michelle Xie, Camilo Ruiz and more!
Disclaimer: All of these are my personal opinions, and we are open to feedback and new ideas. These are not all encompassing perspectives.