Over the last decade, healthcare data has increased exponentially. From digitizing electronic health records to genomics data, radiology images, and the emergence of wearables, the dramatic increase in data compounded with advances in computing, storage, and networking has put in place a massive, irreversible trend.
For incumbents and the healthcare ecosystem at large, this data holds the key to improving service and lowering cost. However, healthcare incumbents are not technologists. Only a few have deep AI/ML capabilities, leaving a massive white space of opportunity for emerging startups.
Today, my partner Adam Fisher and I are launching a Deep Health Seed Program at Bessemer to partner with the best teams building product at the intersection of healthcare and AI/ML. We will invest an initial $10 million in seed-stage startups with check sizes ranging from a few hundred thousand to a few million dollars. While we don’t intend to take board seats, we will offer our time, counsel, and help as you grow your company.
We have already backed one high caliber technical team out of our new program: Subtle Medical. Co-founded by Enhao Gong and Greg Zaharchuk MD/Ph.D., Subtle Medical leverages deep learning to enhance the quality and speed of medical imaging exams. The company’s software can decrease MRI and PET exam durations by 4-fold, enabling hospitals and imaging centers to reduce patient exposure to radiation and increase the total number of patients that can be scanned daily. Subtle Medical is a fantastic example of a company using advanced computing technology to improve healthcare workflow and provide very compelling, real-time ROI for customers!
Bessemer has invested in seed-stage companies across all sectors since the founding of the firm over a half-century ago. And we’ve long been an early partner to healthcare startup founders. We led the Series A of Allena Pharmaceuticals, Alcresta Therapeutics, Groups, Bright Health, Docent Health and now public OvaScience, Inc. (OVAS), Verastem Oncology (VSTM), and Flex Pharma (FLKS). For Adam and me, in particular, this program is about supporting the next-generation of founders in this space because we believe AI/ML, when effectively applied to healthcare, will have a dramatically positive impact on our troubled healthcare systems.
We are particularly excited by the potential for companies in these five areas:
- WORKFLOW: Hospitals are under tremendous pressure to provide high-quality services at sustainable margins. Automating back-office tasks can be a massive driver of efficiency, is technically feasible, spans across all areas of healthcare operations, and has a clear ROI for the buyer. Our company, Qventus, is an excellent example of a team creating tremendous efficiencies in hospital operations by optimizing patient flow.
- DIGITAL DIAGNOSTICS: One of the most active spaces within healthcare has been using AI/ML to help diagnose diseases. Two strategies have emerged: completely replacing the physician and human-assisted AI diagnosis. While regulatory and GTM are evolving, in the near-term (3–5 years) we see human-assisted AI being the most successful given this technologies promise to augment and enhance current clinical workflow and capabilities.
- POPULATION HEALTH 2.0: There is a clear opportunity to better classify patients based on their risk. Previous incarnations of “big data” companies presented pretty dashboards that stratified risk, but did not engage users nor lead to better outcomes at lower costs. We see an emerging opportunity to use ML/AI to transform clinical insights into action, delivering on the promise of population health.
- COMPUTATIONAL BIOLOGY / DRUG DEVELOPMENT: Given the rise of genomics information, EMR data, and emerging real-world evidence data, many ML/AI companies are focused on the drug development space. The promise of these companies is intriguing: collect vast amounts of data, apply ML/AI techniques, and new mechanisms of action and drug targets may emerge. However, many large pharma companies are already using computer-aided drug design techniques, and the unmet need is not yet clear. Rather than pure SaaS business models, we continue to believe the standout companies in this space will look more like drug discovery platforms and traditional full-stack biotech companies. Entrepreneurs in the ML/AI drug discovery space should bolster their drug development teams and prepare for large, biotech-like financings.
Separately, we believe there’s an opportunity for ML/AI to improve clinical trial efficiencies and enhance commercial teams’ effectiveness. Both of these areas have big budgets and clear ROI, which offers a fertile hunting ground for emerging startups.
- TREATMENT: Companies that focus on augmenting treatment protocols are going to face the most challenges given the significant regulatory hurdles, reimbursement challenges, and difficult sales cycles. That said, entrepreneurs who successfully navigate these waters and apply ML/AI to improve outcomes at lower costs will have a significant impact on our healthcare system.
We are thrilled about the burgeoning number of highly talented teams across the globe (from Herzliya to Boston to Silicon Valley) focused on applying AI/ML to healthcare. The future of frontier computing in healthcare is awe inspiring. If you are an entrepreneur building in this space, we would love to hear from you!
Read more about our Deep Health Seed Program in TechCrunch.