Pre-prescription churn: why patients leave before treatment starts

Pre-prescription churn is the drop-off between a patient signing up and ever receiving their first prescription. It is not the same problem as patients quitting in month two, and it is usually not one problem at all. In my clinical experience it is four different problems wearing one dropout number: the patient wasn't educated about the medication, the price surprised them, they weren't the right candidate, or they didn't meet the criteria. Two of those are marketing's to fix. Two are clinical, and no landing page has ever fixed a clinical problem. This page is about telling them apart.

What is pre-prescription churn?

It's the gap most dashboards can't explain: someone clicked the ad, completed signup, maybe even finished intake (the questionnaire and screening a patient completes before a clinician evaluates them), and then never became a treated patient. Marketing sees a signup and a disappearance and calls it a funnel problem. What actually happened sits in the clinical window in between, which is the part of the company most growth teams have never watched. This is a different page from why retention drops off after month 2. That one is about patients who started treatment and left. These patients never started, and the fixes have almost nothing in common.

What happens in the window marketing can't see?

Here is how I described it when asked directly, and I'm quoting myself because the list matters more than polish:

"So there's a couple different things: Potentially the patient wasn't well educated on the medication. Maybe it was too expensive. Maybe they were not the right candidate. Maybe they didn't meet the criteria for that medication. I think pre-qualifying the patient or qualified sales lead, for example, is really important. I think often marketing isn't talking to sales, so it's hard to know exactly. They don't know the full loop of what's going on, and I think giving them that data and looking at what's actually happening would be helpful."

Four causes, one number. That's the whole problem: the dropout total counts them together, and the four need opposite fixes.

The four causes, and who owns each one

Education, owned by marketing. The patient arrived not understanding what the medication involves: how it works, what the first weeks feel like, what commitment they were starting. They learn during intake, get uncomfortable, and leave. The fix is honest expectation-setting before signup, not after.

Price, owned by marketing. The real monthly cost, the labs, the visit fees became clear after the ad's number. The patient did the math and left rationally. The fix is pricing clarity early enough that the people who can't afford it never enter the funnel and the people who can, trust it.

Candidacy, owned by the clinic. The patient could safely take the medication but it wasn't right for them: their situation, their history, their goals. A clinician made a good call. This dropout is the system working.

Criteria, owned by the clinic, and caused upstream by the ads. The patient never met the medical requirements for the treatment at all. A clinician screened them out, correctly. But notice what really happened: the ad recruited someone who was never eligible, and the company paid acquisition cost for a patient it could never treat. The clinical decision was right. The marketing that produced it was waste, and it shows up later as rising acquisition costs nobody can explain.

Why "fixing the funnel" doesn't move the number

Because the funnel fix only touches two of the four causes. A team that sees one dropout number responds the way teams do: rewrite the landing page, shorten intake, add reminder emails. Education and price dropouts might improve. Candidacy and criteria dropouts won't, because they end at a clinician's correct decision, and no amount of funnel polish changes who the ads recruited. So the number barely moves, the team concludes the funnel work failed, and they buy more traffic to compensate, which recruits more ineligible patients and makes the blend worse. The dropout number isn't a dial. It's four dials, and two of them are supposed to be where they are.

How do you find out which cause you actually have?

Split the dropouts by where they stopped and what the clinical side recorded. This requires the thing my own answer above flags: marketing talking to the clinical side, with the loop closed by data instead of guesses. Patients who left during education-heavy steps point one way. Patients who left at the price reveal point another. Patients a clinician screened out belong in their own two buckets, labeled as clinical outcomes, not funnel failures. Every company's split looks different, which is why I won't print a benchmark; I haven't seen a credible public one, and this page doesn't invent numbers. What I can tell you from the clinical side of that wall: the split is never what the marketing dashboard assumed, and the criteria bucket is nearly always bigger than the team expects, because it's invisible to everyone who can't read the chart.

What this means for your ads

The most expensive fix in telehealth marketing is qualifying patients the clinic can never treat. If the criteria bucket is large, the answer isn't a better funnel, it's better pre-qualification: honest eligibility language in the ad and on the page, even though it lowers click-through, because the clicks it removes were purchased waste. This is the rare case where marketing improves by shrinking. Whether the remaining spend is justified at all is the question answered honestly in what acquiring a telehealth patient actually costs.

FAQ

Is pre-prescription churn the same as intake abandonment?

Intake abandonment is one slice of it: people who quit mid-questionnaire. Pre-prescription churn covers the whole window from signup to first prescription, including patients who finished intake and were screened out by a clinician.

What's a normal pre-prescription drop-off rate for telehealth?

There is no credible public benchmark, and this page won't invent one. The useful work isn't comparing your number to an average; it's splitting your own number into its four causes, because they need different fixes.

Can better ads really reduce clinical screen-outs?

Yes, at the recruiting end. Ads and landing pages that state eligibility honestly recruit fewer people the clinic must turn away. The screen-out itself is a clinician's correct decision; the goal is paying for fewer of them.

This page describes patterns from clinical practice, clearly labeled as such; it cites no cohort study because I haven't found a credible public one, and it invents no numbers. It is marketing operations guidance, not medical or legal advice.