Machine vision + empathy at the bedside

Why we invested in Swift Medical

Data Collective (DCVC) recently led the series A funding for Swift Medical alongside great early-stage investors Relay Ventures and Real Ventures, among others.

Swift applies the best of machine vision, on the smartphone, at the bedside to take on the massive cost and suffering that comes from poorly executed wound care. And it’s not theoretical- Swift is the wound care layer of choice in skilled nursing facilities (SNFs) and specialized wound care centers throughout North America, already booked into 1,000+ institutions and monitoring more than 100,000 patients each month. This series A is about execution, company-building, and scale.

Swift is a great example of what we at Data Collective call Computational Care, applying our deep tech and deep compute philosophy as close as possible to the patient journey. Computational Care envisions the future of health provision and care being rebuilt on the pillars of advanced compute, empathy for the patient and caregivers, and scientific evidence. In the case of Swift, this philosophy is applied to the health extender at the bedside- the nurse, the technician, the home health aide- to provide world-class wound surveillance, tracking, documentation, supplies-ordering, and clinical care prediction and planning.

Wound care really matters. There are nine million people in North America living with a chronic wound, 40 million beds requiring wound monitoring, and 400 million global wound care patients per year. Providing effective care starts with the accurate measurement and categorizing of their wounds.

And yet, 40% of wounds are mis-categorized with some clinically relevant error.

Wound care errors drive higher system costs and negatively impact clinical and system quality, harming provider reimbursements, inviting further regulation, driving administrative costs, system complexity, and redundant clinical costs. Clinically- it’s hard to properly treat and follow-up when you inaccurately diagnose and document.

For families, bedside wound care evokes anxiety. “How do I know they are on top of his wounds? Measuring and tracking them accurately?” If they knew the above 40% error rate, they would be more concerned. Knowing your loved one is receiving robust, digitally tracked and scored wound care brings calm and assurance.

To make things worse, the aging curve tsunami, along with the severe supply shortage of experienced eldercare and post-acute workers with a loving touch, threaten to exacerbate this quiet but systemic problem.

Fortunately, there’s Swift.

Swift is not special because of the near science-fiction quality machine vision, trained now on millions of images, led by a founding CTO who grew up applying vision intelligence to NASA’s Mars Rover.

Nor is Swift special because it is delivering for demanding enterprise customers that run hundreds of post-acute and skilled nursing facilities and hospitals and home health agencies.

Swift is not special because it works so tightly with EMRs and the supply chain to scale this to market rapidly.

Swift does all of those things, yes, but Swift Medical is special because it applies all of those things with empathy to the patient and within the context of patient care and workflow. And, it turns out, shining a light on an error-ridden process drives continual improvement, lessens neglect, and steps closer to optimized care with fewer bureaucratic burdens.

Fundamentally, Swift enables wound care quality that all of us would want for a loved one.

We will continue to have many twitterati debates about AI and healthcare- what is hype? what is real? are we replacing or augmenting doctors?- but Swift exemplifies a set of use cases in healthcare about which we should all want machine progress, which is turning health extenders into superheroes in a way that improves clinical and operational processes.

Two final points:

  • In Computational Care, we invest in CEOs that exemplify the attributes and build teams with excellence in compute + empathy + evidence. But, for a series A, we also need to see accelerating execution. Meet CEO Carlo Perez.
  • Toronto- vibrant, efficient, diverse teams, product-centric, infused with data and intelligence, close to and overlapping with world class academics… similar to our expansion work in @element_ai and other future Toronto and Ontario investments, this is a great neighborhood to build teams and product to scale to North America and then globally. DCVC is open for business in any city where founders have access to this type of talent.

Scott Barclay is a partner at Data Collective (@DCVC), can be reached at and really should tweet more @SABarclay