Flywheels in Mental Health Tech
A few months back, I had a casual chat with a product leader at a high-profile startup. We got to talking about frameworks, and he showed me a diagram of the flywheel his team had mapped out for their product. I was immediately struck by its elegance and power. I had heard of flywheels before, namely from reading the seminal book Good to Great, which popularized the term. However, I had never really taken the time to dive into the concept and examine how it applied to mental health tech and Limbix’s products in particular. I resolved to do so.
So, what is a flywheel?
The term flywheel refers to a positive feedback loop in a product or business that makes it progressively easier to increase revenue and fend off competition. Think of a heavy wheel that is difficult to spin at first, but as it gets going, starts to build on its own momentum, and becomes easier and easier to accelerate. If you’ve ever used a spin bike, you’ve experienced this for yourself. The bike’s internal wheel is literally called a flywheel.
Flywheels are powerful. Virtually every one of the world’s most valuable and enduring companies has leveraged them to achieve success.
So, it came as no surprise to me that flywheels can (and must) be leveraged to achieve success in mental health tech.
I’ve identified four types of flywheels that occur frequently in mental health tech:
- Shared economies of scale. For example, when a mental health company invests in technology to lower the marginal cost of care. If the lower cost is passed on to patients in the form of lower prices, it attracts more patients, which drives down per patient costs even further.
- Network effects. For example, when two-sided marketplaces are built to match therapists with patients. More patients attract more therapists, which attracts even more patients.
- Brand habit. For example, when a digital therapeutic becomes associated with the treatment of a specific diagnosis, that therapeutic is more likely to be recommended by healthcare providers and used by patients, which strengthens the association even further.
- Switching costs. For example, when a product gets integrated into a health system’s electronic health record (EHR), it becomes costly for those health systems to switch to another vendor. This reduces competition, making it easier for the existing vendor to upsell and cross-sell new products to the health system.
Shared economies of scale
Economies of scale occur when increased volume reduces the cost of production. They’re usually associated with the manufacturing of physical goods, which has high initial fixed costs (building the manufacturing plant), followed by low marginal costs to produce items within the plant.
The flywheel effect occurs when companies share their economies of scale with their customers in the form of lower cost or greater value. Think of Costco, which shares its economies of scale with customers in the form of lower prices. The lower prices attract more customers, which increases the economies of scale, allowing Costco to lower prices even further.
Mental health example: Tech-enabled mental health providers
Affordability and lack of access are among the greatest challenges in mental health care today. There is an overwhelming need for care combined with a severe shortage of mental health care providers. This classic supply-demand mismatch results in only a small percentage of those in need accessing care, and those lucky enough to do so end up paying extraordinarily high out-of-pocket prices.
This problem, in large part, stems from the economics of traditional face-to-face therapy. Therapy has high variable costs: regardless of volume, the hourly cost to deliver traditional therapy stays roughly the same.
Tech-enabled mental health providers offer therapy, but leverage technology to lower the cost of care and improve access. This includes:
- Automating administrative tasks. Technology can be used to automate time-intensive administrative tasks ranging from appointment scheduling and note-taking to billing and processing insurance claims.
- Delivering care through coaches. Many tech-enabled mental health providers are improving access by delivering care through coaches — many of whom receive on-the-job training — rather than Master’s and Doctoral level providers. The coaches deliver highly manualized therapies, and technology is used to facilitate strict adherence to protocols.
- Incorporating software-based treatments. There is a growing body of evidence showing that stand-alone and guided1 software-based treatments deliver clinically significant outcomes for patients. This is particularly so for patients with lower-severity symptoms. Many tech-enabled mental health companies are incorporating software-based treatments to lower the overall cost of care.
In all of the above cases, the technology has considerable initial fixed costs. However, it becomes minimal when spread out over a large patient base. Over time, these initiatives drive down the per-patient cost of care.
A flywheel effect can occur if tech-enabled mental health companies share these economies of scale with payors and patients. The most obvious way is to reduce the price of care. Doing so expands access to the services, increases usage, and reduces the cost even further.
Companies can also share their economies of scale by providing more value for the same price. Think of Netflix, which continually reinvests its profits into producing more and more content, and thereby delivers more value for the same price. So, too, tech-enabled mental health providers can reinvest profits into the creation of more value for patients, such as more self-guided content or on-demand access to coaches. Doing so attracts more users, which further lowers the per-user cost of delivering care.
Network effects
Network effects refer to a mechanism in a product in which every new user of the product makes it more valuable for everyone else. While there are many different types of network effects, the types I see most frequently in mental health tech are two-sided network effects and data network effects.
Two-sided network effects occur when there are two categories of users (e.g. riders and drivers on Uber). More supply-side users (e.g. drivers) attract more demand-side users (e.g. riders), which in turn attract more supply-side users.
Data network effects occur when new data makes the product more valuable, and increased usage of the product yields more data. A classic example is the navigation app, Waze. Users create traffic data, which produces better route calculations, which encourages more usage, which creates more traffic data.
Mental health tech examples
Two-sided network effects: Therapy marketplaces
In mental health tech, the two user groups are usually therapists and patients. Many of today’s most valuable mental health startups, such as Talkspace, Lyra, and Headway, operate as 2-sided marketplaces that match these two groups.
Therapists prefer the platform that offers them to build the largest caseload with the least amount of effort.2 Patients, in turn, prefer the platform that has the best selection of therapists. Thus, more therapists on the platform attract more patients, which in turn attract even more therapists.
While therapists and patients can switch between marketplaces, the two-sided network effects operate such that only a small number of marketplaces will achieve the critical mass of therapists and patients necessary to sustain themselves. Just as we are left with Uber & Lyft, DoorDash & Uber Eats, and Airbnb & Vrbo, we will be left with just a few dominant therapy marketplaces.
Therapists also prefer the platform that offers them the highest rates. Platforms that have more therapists can leverage their scale to negotiate higher reimbursement rates with payors. For example, Headway is a platform that connects patients with in-network therapists. As it increases the number of therapists on the platform, it has greater power to negotiate rates with insurers (e.g. Anthem or United Healthcare), which it can then leverage to attract more, higher-quality therapists. In this case, there is a combined effect of shared economies of scale (from the platforms negotiating power) and two-sided network effects.
Data network effects: Digital biomarkers
Mental health providers have traditionally relied on subjective measures of clinical effectiveness. For example, the primary measure of depressive symptoms is the PHQ, which requires patients to answer questions like “How often have you felt down, depressed, or hopeless in the last two weeks?”
Digital biomarkers refer to technology that measures mental health symptoms based on objective and passive user data. Companies like Ellipsis Health and Kintsugi quantify symptoms of anxiety and depression based on patient speech data. Others like Mindstrong and KeyWise are aiming to do so using smartphone interaction patterns like typing, swiping, and scrolling.
These products leverage algorithms trained on large amounts of proprietary data. Product usage leads to more training data, which improves the accuracy of the product. This leads to greater uptake of the product, which leads to even more data.
If a digital biomarker amasses proprietary training data that materially improves product quality relative to competitors, it can produce a flywheel effect that is very difficult for competitors to catch up to.
As of this writing, Apple just announced its entry into the digital biomarkers space. Apple’s vast access to data may prove to be an insurmountable advantage for the existing players to compete with.
Brand Habit
Brand habit refers to the positive association between a product and its value proposition. When a customer has a positive experience using a product it strengthens that association, increasing the likelihood that they’ll use the product again. At the same time, prospective new customers vicariously learn the brand habit from existing users, making it more likely that prospective customers will convert.
Mental health tech example: Employer-funded mental health benefits
As I’ve written about before, self-funded employers tend to be early adopters of mental health tech products, and they account for a significant proportion of the industry’s revenues.
Brand habit impacts the likelihood that a self-funded employer will choose to offer a specific mental health product to their employees. For example, benefits directors (the decision-makers) read case studies, go to benefits conferences, and network with their industry counterparts. The more employers that offer a specific product, the more likely it is for additional employers to follow suit due to the strong influence of social proof and bandwagon effects.
There is also a simultaneous brand habit impacting employees. In most cases, self-funded employers only pay for products when their employees use them. Therefore it is not enough to get the employer to offer the service (the first sale) — a secondary sale is needed to activate each employee and drive usage. As employees start to use the product and develop a brand habit, they are more likely to continue using it. At the same time, their colleagues will learn about the product from their peers, and be more likely to use the product as well.
Switching costs
Switching costs refer to the loss in value that a customer incurs when switching to an alternative supplier.
New customers often invest considerable resources in integrating a product into their existing systems. If the customer then wants to switch to a different provider, they must incur that cost all over again.
Furthermore, if that customer wants to buy related products, there is a distinct benefit to purchasing from an existing vendor. This is especially so if the new offering seamlessly integrates into the vendor’s products. This dynamic lowers customer churn and makes it much easier for vendors to upsell and cross-sell products.
Mental health tech example: Selling to health systems
When a health system purchases a new product, it must obtain signoff from a complex group of stakeholders, including clinical, legal, finance, and IT. Then, once the vendor is chosen, significant resources are invested into training staff and integrating the product into the health systems workflows.
Health systems are therefore reluctant to replace an existing product with a competitor unless it’s significantly better or less expensive. They’re also more likely to purchase new products from existing vendors because those new products will more easily integrate into their existing workflows.
This is one of the reasons why many large tech companies have recently acquired behavioral health products. Take the merger of Teladoc and Livongo. Teladoc already serves many of the nation’s largest health systems. The merged entity can now easily cross-sell Livongo’s behavioral health offerings to Teladoc’s customers. The recent acquisition of Silvercloud by Amwell is motivated by a similar strategy.
Understanding the theory is just the beginning
In this article, I’ve mostly explored the theory of flywheels. Using this information, I hope that you’ll be able to identify some of the flywheels that exist in your own businesses.
However, identifying flywheels is just the beginning. The real value comes from translating that knowledge into actionable insights to inform product strategy.
In a follow-up post, I’ll walk through a case study and map out multiple flywheels in a single product. I’ll then explore how the flywheel map indicates where to invest for maximum leverage and the selection of a ‘true north’ metric.
Additional reading
If you’d like to learn more about flywheels, I highly recommend the following resources:
- Advantage Flywheels by Max Olson. This is a great primer with excellent diagrams of well-known companies’ flywheels.
- Flywheel Effect: Why Positive Feedback Loops are a Meta-Competitive Advantage by Eric Jorgenson. Another great primer on flywheel effects.
- Scale Economies Shared by Jonny Cornish. A fantastic deep dive into shared economies of scale.
- The Network Effects Manual by James Currier. The best article I’ve read on network effects.
- 7 Powers: The Foundations of Business Strategy by Hamilton Helmer. While not specifically about flywheels, this covers many of the strategic forces that underlie flywheel effects, such as brand, switching costs, network effects, and economies of scale.
- Thinking in Systems by Donna Meadows. At their core, flywheels are systems of positive feedback loops. This classic on systems thinking provides excellent foundational knowledge about how positive feedback loops accelerate and then ultimately decline.
Thank you to the following people for valuable input on drafts: Ben Lewis, Jon Sockell, Elise Ogle, Mel Goetz, Amanda McCawley, Tina Quach, Kabir Daya, Meeta Sharma, Eugene Hauptmann, Nithya Krishnamoorthy, Marcus Whitney, David Burt, Jordan Jones, Soma Mandal, Kavir Kaycee, Melissa Trevino, Angelo Belardi, Chris Angelis, Kiki Schirr, and Christine Cauthen.
