For a health app to be successful, it needs to have a high level of engagement, but that doesn’t necessarily mean it will be profitable. Many teams that have developed monetization muscle based on consumer software find this out the hard way, months after a device or app have been adopted by users.
It’s not a demand issue. It’s about the real world of healthcare buying, and what regulation will allow once the real data of usage begins to flow.
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The reasons why HealthTech monetization doesn’t follow consumer app rules
Consumer apps are impulse and habit driven. Healthcare products go through a trust and procurement process and may be subject to an insurance company or employer between the app and the user. The B2B2C sales motion may take quarters to close and the reimbursement time is even longer, meaning that the revenue is not on a timetable that any consumer growth model would expect.
The downside of churn is low, so freemium is a good strategy to use for fitness apps. Clinical grade tools are liable, and if they are underpriced, then their free offering may diminish the credibility of the paid offering. HIPAA, GDPR and MDR also limit the packaging and resale of user data, which is how many consumer apps are funded by ads and monetization of data. Part of the reason that direct-to-consumer health apps often transition to enterprise or payer deals within 2-3 years is that no one individual consumer was going to foot the bill for compliance.
How to make the revenue models that stick in reality
There’s a reason why health apps have a tendency to hit the plateau in their subscription numbers: engagement fatigue sets in once the acute issue is addressed and churn climbs just as the clinical value is said to be proven. This can be avoided by B2B2C licensing to an employer or health system, where the buying decision lies with an organization with a longer time horizon.
It’s more difficult to define outcomes-based payment prices, as they must be based on a measurable outcome that can be defended, but they can be more easily adopted once a health system has confidence in the data supporting them. Data licensing and research partnerships are still possible, albeit in a limited and highly regulated way, in the form of de-identified, consent-limited and audited data. Hardware plus software products have lower margins than software alone products, but can close sales that software alone can’t.
The Internet of Things is not only a feature, it’s a monetization layer
A one-time sale of the remote patient monitoring device means that most of the money available is left on the table. The value is in the data continuity: the same value coming from the same device over months and months, not the device itself. The continuity is translated into a consistent and repeatable revenue stream by consumable parts such as sensors and patches.
How well the device integrates with the app is a key factor in retention. If the Bluetooth connection drops or the sync is slow, it’s not just a bug in this category, it’s a reason a patient discontinues use of the device. It’s not just the HL7 FHIR and Bluetooth LE health profiles that are being seen as a minimum requirement for enterprise buyers, though, they are also becoming a standard part of the deal.
The Engineering that is the foundation for monetization
Consent, audit trails, and clinical data are all handled correctly on top of which pricing experiments can be made. Teams might be surprised to learn that the scope of healthcare mobile applications development in the healthcare sector is more realistic, as things like consent management, audit logging, and clinical data handling have to be built before the first pricing tier is finalized, for example.
Patient-facing UI is not an easy thing to do. The steep learning curve on clinical interface conventions makes it difficult enough to demand that many teams hire front-end developers who have prior healthcare UI experience, instead of generalists, for dashboards, device-pairing flows and accessibility requirements.
Technical debt tends to become more apparent as it accumulates early, especially when it comes to data models that are not designed to be audit friendly, only for a team to have to rebuild at the last minute when they’re about to launch a new pricing tier. The first six months is the time when founders have to relax about vendor and staffing choices, and once they have been made they will be stuck with them for a long time.
A few things to know before committing to a metric
In this category, there’s little to be gleaned from raw daily active users. How engaged a patient is (consistency of logging readings) and how often a clinician actually sees the dashboard predicts renewal much better. The payback period for B2B2C health deals can be worse than the consumer payback, as the sales cycle and implementation time are both before revenue is recognized.
The most prevalent failure mode is scaling acquisition prior to the compliance infrastructure that can support it, thereby creating a burden and not a benefit. The same reason for pilot to scale transitions with health systems to stall – pilot demonstrated clinical value, not operational readiness at scale. If a monetization model requires continual adjustments instead of a single solution, it’s likely that the monetization model itself needs to be adjusted, not the execution.
Conclusion
The ones that make money from HealthTech tend to see compliance and integration as the bedrock that the business is built upon, rather than a cost center to cut down on prior to a fundraise. If you do this properly, the pricing discussion is much easier than most founders anticipate.
