Revenue Is a Misleading KPI in Healthcare

In most businesses, revenue is a straightforward measure of how well you’re doing. It usually tracks some version of value delivered, where more customers lead to more usage and more revenue. However, in healthcare, the blind pursuit of revenue can cause real harm.

Although a revenue number tells you that a transaction happened, it does not tell you whether the patient was appropriate for the service, whether the care plan was clinically sound, whether the patient stayed in treatment long enough to benefit or even whether the business is building trust or quietly eroding it. If the transaction and outcome have a gap in between them, you’re in trouble.

This issue is exacerbated with telehealth. The model is fast, measurable, and easy to grow on the surface. The acquisition data comes in quickly. The conversion funnels look sharp, and the volume of appointments makes you feel like you’ve gained traction.

Many telehealth companies learned this the hard way. Raw acquisition volume can look like an obvious top-line KPI because more new patients seem to mean more demand, more revenue, and more apparent progress. But over time, that number can start shaping the wrong behavior. It can reward looser intake criteria, make lower-quality demand easier to tolerate, and push organizations toward quantity rather than patient fit.

That is a risky drift in any area of healthcare, but especially in mental health, where clinical trust and continuity are the bedrock of service. They decide whether care works at all.

This is one of the biggest differences between healthcare and other industries. Every healthcare company is really running two systems at once. One is the business system, with all the familiar concerns around growth, acquisition, retention, and unit economics. The other is the clinical system, which has its own logic, standards, and constraints. When those two systems stay aligned, the company becomes stronger over time. When they run contrary to one another (as is the case when business priorities are given precedence over clinical standards), the damage shows up as softer intake, weaker patient fit, higher churn, more pressure on providers, and a growing temptation to make clinical tradeoffs in service of a business target.

That pattern played out across much of the telehealth boom. Too many companies optimized for acquisition and retention without asking whether the patients entering the system were actually the right patients, or whether the model could preserve clinical standards as it scaled. For a while, the numbers could still look good. Revenue grew, new users arrived, and the company appeared healthy. Underneath that surface, though, many of those models were under strain.

At some point, telehealth companies face a version of this choice: you discover a portion of your patient volume shouldn’t be there. The question is what you do with that information. If a company tightens its intake standards or puts stronger safeguards in place to detect drug-seeking and diversion behavior, it may lose a measurable portion of patient volume in the short run. That might look painful on paper, but the tradeoff is often healthier provider time allocation, cleaner outcomes data, less vulnerability to misuse, and a care model that behaves more like a clinic and less like a transaction engine.

That is why a better top-line signal is often retention-weighted cohort quality rather than raw acquisition volume. A patient who stays in care for twelve months is not just worth more financially than someone who churns after one visit. That patient is evidence that the intake was appropriate, the care model fit the need, the provider relationship held up, and the service created enough trust to continue. In healthcare, that matters way more than revenue.

The rest of the metrics should follow the same logic: good metrics should help you make better decisions. A useful framework is to separate business performance metrics from clinical quality metrics and to pay equal attention to both. The standard business numbers, including ARR growth, blended LTV:CAC, payback period, provider utilization, retention by cohort, and CSAT, can not be ignored either because they show how the organization is performing. But companies also need leading indicators of care quality. Clinical adherence scoring, for example, can indicate early on whether care is being delivered consistently and whether problems are starting to emerge before they show up in patient outcomes or compliance issues.

Just as important is knowing which metrics to ignore. Providers can sometimes struggle with too much data, making decision-making gets all the more difficult. Vanity engagement metrics are especially misleading in healthcare. Time on app, page views, session depth, and similar activity numbers may look useful, but without outcomes, they tell you very little. In some cases, they tell you the wrong thing. A patient spending more time in the funnel does not mean the system is more effective. A product with high engagement does not necessarily have a sound care model behind it. 

That is why clinical separation has to go beyond rhetoric to the actual structure of it all. Clinical decisions cannot be bent around whatever improves the dashboard this quarter. A patient may leave disappointed because they did not get the treatment they expected, and that may still be the correct clinical outcome. If a healthcare company is unwilling to absorb that tension, it will eventually start making the wrong decisions for the wrong reasons.

Over time, the organizations that shift their focus away from pure volume business are the ones that hold up. There is a meaningful difference between a platform that processes visits and a clinical organization that delivers care well. You can see that difference in intake standards, in provider workflows, in which patients stay, in what gets measured, and in whether leadership is willing to accept short-term revenue pain to protect long-term clinical integrity.

That is why revenue, on its own, is such a misleading KPI in healthcare. It can make a weak system look strong for much longer than it should, and it undermines the entire thesis of providing healthcare.

Photo: Thanasis, Getty Images

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