Investing In Pure Health Tech

Define Ventures published an interesting report on venture-backed health tech investment performance titled Health Tech's Defining Decade. They point to a few relevant stats:

• As of 2020, health tech makes up 10% of all venture funding. 

• In 2024, there was $18.6 billion in health tech venture investments. 

• 10% of private unicorns (companies with valuations over $1 billion) are in health tech. 

• Since 2020, 18 health tech unicorns have exited (through IPO or M&A). See full list below.

I like the way they segmented the 18:

1/ SaaS (pure tech, software) - 1
2/ Services (serving patients) - 7
3/ Hybrid (mix of SaaS and service) - 7
4/ Payer - 3 

This data speaks to the challenges of investing in pure software health tech companies or vertical SaaS in general. 

With a couple of exceptions, the services and hybrid companies are mostly telehealth and traditional provider organizations with some tech that improves the patient or provider experience (I probably would've put Health Catalyst and Progyny more towards the SaaS, pure tech group and possibly Fitbit — while they're a hardware company, their profits are primarily software-based). 

The payers are, well, payers with some tech to drive better outcomes.

The only pure SaaS company they list is Doximity, and while they are an incredible company, you shouldn't really think of them as a health tech company; they're an advertising platform for pharma companies. Again, they're a fantastic company and definitely support the healthcare ecosystem, but the vast majority of their revenue comes from the same budget pool as Instagram and Snapchat.

It's now become sort of a common trope for health tech companies to build a software product and grow really fast with nice high gross margin revenue only to find out they've run out of TAM (total addressable market) and need to pivot to lower margin services to continue to grow, and by services I don’t mean managed services or software consulting, I mean doing the actual work that their customers do. And that's consistent with this data. It's really hard for pure software companies in healthcare to get big. They can’t just provide software to their customers who do the work; they have to do the work, too. While people will talk about healthcare as a $4.5 trillion industry, healthcare IT represents just 4% of that, or about $180 billion. Most of the TAM has already been eaten up by EHRs, telehealth, analytics, and health information exchange. Further, the software infrastructure that large healthcare organizations use is dominated by several large horizontal software vendors — AWS, Snowflake, Salesforce, Symantec, Okta, etc.

So, the opportunity largely exists in either unseating the large incumbents in some form or in targeted areas, such as clinical/operational efficiency, patient experience/engagement, analytics, etc., but again, you're likely going to run into a TAM problem. 

Of course, all of this brings us back to AI and how it will impact health tech and health tech investing, as that’s probably the best opportunity for more pure tech companies to achieve service-like valuations. And there are probably more questions about this topic that we haven't even thought of yet than the ones we have. My sense is we'll find that these products really impact margins more than TAM, but who knows? Regardless, as AI emerges in health IT, new frameworks will be required for how to think about these opportunities. I'll be thinking about that a lot and posting those thoughts here.

Life Advice

A few weeks ago, I had lunch with my Godson, a sophomore in college. Towards the end, he asked, “Do you have any career advice for me?”

I immediately thought of this blog post on Capital Gains that talks about the dangers of giving life advice based on your own experiences. From the piece:

"…agreeable extroverts will probably tell you that the best thing you can do for your career is to meet as many people as possible, so you have lots of second-degree connections through which you can hear about opportunities. This is probably true for them, but someone who naturally loves nerding out about esoteric programming languages would dismiss this advice out of hand—meeting people is hard, getting them to like you is harder, so you should focus on simple, achievable goals like getting a thousand stars on your Github repository.”

Success comes from a combination of hard work, luck, and skill at the work one is doing. However, a less often discussed component is our inherent temperamental traits. In other words, depending on who you are, some stuff is easy for you, and other stuff is really, really hard. And this matters a lot.

Personally, it’s very easy for me to wake up in the morning and go to the gym. I almost don’t even think about it. I just go. On the other hand, it’s very difficult for me to keep in touch with friends and acquaintances. It takes lots of work.

I have a friend who is the exact opposite. He has struggled his whole life to find a way to build a consistent workout routine. I’ve seen him try enough to know that I don’t have more motivation or willpower than he does. I’m certain of it. It’s just inherently easier for me to do the thing we’re both trying to do. Conversely, he effortlessly keeps in touch with a huge network. Regular phone calls to friends when he’s driving. He stops in on people when he’s in town. Remembers birthdays. Sends gifts. I have to really work at it. It’s hard for me. Of course, it’s important to him, and he does it with purpose, but I can see that it’s also easier for him than it is for me.

Obviously, this applies to our work as well. So if you advise someone to get into the legal profession and, despite your personal success in that field, they have a really hard time consuming large amounts of complex text for long periods of time, all else equal, that person is really going to struggle, and you’ve given them really bad advice. Similarly, if you tell a math genius who is shy to get into a sales job early because that’s where the money is, you’re really setting that person back. Both because they’ll really struggle at the job and because they’ll suffer from a very high opportunity cost of not building multimillion-dollar machine learning models for an AI company or something like that.

People giving career advice are often biased and tend to lean towards their unique, long, and winding path to success. That’s a bad idea. The most extreme example is “follow your passion.” This is great advice if your passion is financial analysis, but not so good if it’s skateboarding.

All of this is to say that giving career advice is really hard, and we have to be cautious about not sending younger people down our paths because what worked for us in so many ways may never work for them.

A while back, I wrote a summary of my best career advice at the time titled 15 Things I Wish I Knew Before I Started. I went back to take a look at it to see if I was guilty of giving biased career advice. It's not perfect, but I was happy to see that it holds up fairly well.

Anyway, if you’re wondering what I told my Godson, I came up with two pieces of (hopefully) unbiased advice:

1/ Pursue opportunities with leverage (adapted from Naval Ravikant). You may not be able to do this in your first job out of college — honestly, your first few years are probably going to suck, so you’ll have to keep your head down and grind for a while — but always try to ultimately pursue work where your outputs are disproportionate to your inputs.

2/ Leave something on the table. Leave jobs, partnerships, and business relationships where the other person feels like they got more from you than what they paid. That results in a reputation with a surplus where people will feel good about working with you and will say good things. That surplus compounds. If someone thinks you’re good and they hear from someone else that you’re good, now they think you’re really good, and they’ll tell people that you’re really good, now those people think you’re really good. And on and on. If you do this well, your career will feel less like you’re climbing a ladder and more like you’re sailing on a boat with a steady wind at your back.

Why It's So Hard To Measure Healthcare Technology's Impact

With the thousands of healthcare technology companies that have raised hundreds of billions of dollars in venture capital and other financing over the last couple of decades to make healthcare more efficient, you'd think we'd be seeing the cost of healthcare delivery come down. Of course, this hasn't happened at all. A lot of people like to point to this chart, which shows that the price of things like TVs is dropping while the price of things like healthcare is going up.

This chart has always annoyed me a bit because it lacks so much of the nuance of each of these industries, but let's go with it for a second and understand why the cost of healthcare isn’t dropping like the cost of a television.

Clearly, televisions have become much cheaper and much better over the last several years. Many technological advances such as LCD, OLED, automated mass production, and labor cost reduction have brought down the cost of producing a TV. Add to that significant global competition, perfect pricing transparency, and the right incentives among consumers, and it’s no surprise that prices have dropped dramatically.


Let's apply that logic to healthcare. Imagine a health system that employs 500 primary care providers. They go out and buy AI software products that make their primary care providers, say, 100% more efficient. Because of technological advancement, it now costs the health system 50% less to supply a PCP appointment to a patient. That's great news. But how does that cost reduction actually show up and become visible to an end consumer?



Well, first of all, it's highly likely that rather than reducing costs, consumption would go up, and costs would stay the same if not increase. More appointments mean more patients, which means more downstream referrals to higher-cost services. The end consumer doesn't buy their own personal set of healthcare services like their own TV for their living room; they buy into a pooled group of patients under an insurance plan. If that pool increases their consumption of healthcare services, each person's premium increases. More healthcare consumption means higher insurance premiums. So, it's possible that technological advancements actually increase the cost of healthcare, which is good in some cases and not so great in others.



But put that aside, let's say demand and consumption stay the same, and the health system can deliver on that demand more efficiently because of the AI they've procured and that they have material savings to do something with. If the health system is a for-profit, they might be inclined to return that money to shareholders without impacting the price for consumers. If the health system is nonprofit, they might just reinvest it back into upgrading a building or hiring more specialists, which would also keep prices flat.
When a television company makes a technological advancement that allows it to make better TVs at a lower cost, it might also be inclined to invest in growth or return money to shareholders. However, because of intense competition and price transparency, it is also very likely they will pass those savings on to consumers, lowering the price of the TV for everyone. But health systems don't have the same incentives as the television company in several important ways:

1/ Demand for their services isn't elastic (people don't shop for PCPs on price). 
2/ Their prices are regulated and negotiated with payers and aren't dynamic like television's.
3/ The price actually being paid isn't clear to the patient, as they only pay a portion that depends on their specific health plan.

I wrote about all of these issues in 15 Reasons Why Healthcare Has a Business Model Problem and Healthcare's Incentive Misalignment, so I won't rehash them here. 



The point of this post is that there are a ton of amazing healthcare tech companies that are reducing costs for providers, payers, and patients, improving the quality of life for clinicians, improving the quality of care across the board, and doing absolutely amazing things for the American healthcare ecosystem. But, for the reasons described above, these savings are unlikely to appear in high-level cost metrics. At least not until the incentives change.