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(Where) will money flow into kidney care in 2024?
On growth, efficiency, and capital requirements
Money is a tool. It's the fuel that allows companies to invest in top talent, new products, and future pipelines. It's also the thing that allows founders and fresh ideas to take flight. But despite billions of dollars invested into kidney care over the last few years, 2024 holds no prisoners and makes no promises. Certain sub-segments continue to get disproportionate levels of funding, while others face mounting losses in the so-called "valley of death" on their path to commercialization.
A few weeks ago I shared a glimpse of the tech-enabled kidney care landscape. Today, I thought a more detailed view of growth and funding might be in order, especially now that predictions season is upon us (my poor inbox). So let's talk about the elephant in the room: (where) will money flow into kidney care next year?
What's in this table:
Four (4) segments in focus: (a) value-based care (VBC); (b) remote patient monitoring (RPM); (c) Diagnostics (hardware + software + AI/ML); and (d) Medical Devices (dialysis access + portable machines).
8-10 companies in each segment: focusing on venture-backed businesses with at least 5 FTEs. This is not exhaustive. In fact, several early stage device companies on my radar check one box or the other, but not both.
1-year avg headcount growth, by segment: pulled directly from LinkedIn. It's not perfect. In some cases the 6-month growth number differed significantly from the 1-year, but in all cases they were directionally consistent.
Average funding per employee, by segment: divided the amount of total funding raised (dilutive + non-dilutive) by the total number of employees (per LinkedIn). A (rough) way to estimate and compare capital needs and efficiency across the four segments.
Takeaways
GROWTH
Value-based care remains the fastest growing segment measured by 1-year headcount growth at ~23%. That's pretty significant considering it's also the area of kidney care with the highest average headcount (345 employees) and nearly $2B raised in funding. Remote monitoring (RPM) and Diagnostics (AI/ML) both came in around 15%.
The laggard is the device segment, which came in at ~10%, or about 2.5x slower growth than VBC. But for a category that can take 5 to 7 years or more for products to reach the market, the true growth rate (taking team size, pre-seed and postmortems into account) is likely much lower. Beware selection bias.
CATEGORY LEADERS:
👑 VBC: Panoramic Health
👑 RPM: Kalium Health
👑 AI/ML: DeLorean Artificial Intelligence
👑 Devices: VenoStent, Inc.
CAPITAL EFFICIENCY
It might not surprise you then that value-based care comes in at the lower end of funding per employee, given their focus on operational efficiency, at $388K per employee. Diagnostics ($361K) and RPM ($406K) also came in well below medical devices, which came in at $854K per employee (nearly 2.5x diagnostics).
Now, all that said, this is by no means the gold standard for measuring capital efficiency. It's the investor's equivalent of a finger in the wind. If I had it my way, I'd be using some combination of revenue per employee, cost savings, and burn rate to dial-in on these efficiency metrics. But alas, most of these companies are private. I'll save the public company analysis for a separate post (coming soon).
Higher Funding Per Employee: This could indicate that a company is heavily investing in its workforce, possibly in R&D (i.e. medical device), which could lead to future efficiencies or innovations. However, it could also imply a less efficient use of capital if the additional funding doesn't translate into proportional gains in productivity or revenue. Worth noting several companies in this bucket made significant layoffs in the past year.
Lower Funding Per Employee: This often suggests a lean operation with potentially higher operational efficiency. These companies might be doing more with less, indicating a more prudent or efficient use of capital. However, too little funding could also mean underinvestment, possibly hindering growth or innovation.
CATEGORY LEADERS:
👑 VBC: Evergreen Nephrology (lowest), Monogram Health (highest)
👑 RPM: Proton Intelligence (lowest), Biofourmis (highest)
👑 AI/ML: AvoMD (lowest), ClosedLoop (highest)
👑 Devices: Relavo (lowest), Quanta Dialysis Technologies (highest)
CAPITAL REQUIREMENTS
Keen eyes might have noticed the dotted line running left-to-right halfway down the table. This is a rough split between services and devices. The reason for this is the interplanetary distance between the amount of capital required to stand up a care delivery company vs. bringing a novel medical device to market. Many of you will know this firsthand.
Simply put, not all segments are created equal when it comes to capital requirements. And the hurdles keep getting lower in some areas, and higher in others. It takes about $54 million on average to bring a novel medical device to market. Clearly this requires significant outside capital, often from institutional backers and strategics. Compare that to care delivery companies that mostly have to worry about customer acquisition costs (and some argue are not even venture-backable business models). Or software companies that can scrap their way to sustainable revenue with a lean team and minimum viable product (outside of regulatory pathways).
Bill Hunter wrote one of the best posts I've seen on how the bar has moved for MedTech exits since 1998.
Notes
[1] These groupings are based on primary use cases and value propositions to the best of my knowledge. There are companies with device and/or hardware components within the Diagnostics and RPM categories that likely skew the funding and efficiency metrics (e.g. Renalytix, CloudCath, Proton Intelligence, Alio, Biofourmis) compared to their software-only or software-enabled counterparts.
[2] This is non-exhaustive. I'm focused on kidney care applications. Some of these companies are infrastructure plays with specific products for the kidney care vertical or are simply starting their commercial motions in ESRD before moving on to other indications (e.g. Carenostics, ClosedLoop, DeLorean Artificial Intelligence, AvoMD, Biofourmis, Alio).
[3] My goal, as always, is to share what I learn and how I think about this space. By using readily available tools, hopefully you can take these sources and methods with you into your work. If I missed something or if you think about these data differently, that's what this thread is all about! Please leave a comment so we can open up the conversation and learn from each other.
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