Thinking in Arcs
By Luke Bujarski · March 2026 · 7 min read
For most of the time we ran Chrystal Clinic, we thought about patients one appointment at a time. That's not a criticism. It's just how the operating reality of a clinic is structured. The schedule is organized by appointment. Revenue gets reported by appointment. The booking system treats every visit as a discrete transaction. The patient who came in eight times last year and the patient who came in once look identical in the daily view of the schedule.
Our economic model changed that completely. When we built the patient lifecycle analysis and separated patients by where they were in their relationship with the clinic, we stopped seeing appointments and started seeing arcs. That reframe, from transaction to trajectory, turned out to be the most operationally consequential thing we did.
What an arc actually is
A treatment arc is the natural lifecycle of a patient's care. At Chrystal Clinic, three arc types emerged clearly from the data. Acute patients have a specific injury or problem with a defined endpoint, typically four to eight sessions with a clear resolution goal. Chronic patients have a systemic or long-standing condition requiring sustained treatment over months, with a tapering cadence as stability builds. Maintenance patients are people who had either resolved something or were well and wanted to stay that way, coming in monthly as a preventive practice.
None of this was invented. It was already true about our patient population. What the model did was make it visible and quantifiable. Once we could see which arc each patient was on, we could see where in that arc they were, what the natural drop-off risk looked like at each stage, and what their lifetime value was if they completed the arc versus dropped out at session two.
What changed operationally
The first thing that changed was retention visibility. We could now see which patients were approaching the natural drop-off point in their arc and hadn't rebooked. That's a different kind of alert than "this patient hasn't visited in 60 days." A patient three sessions into an acute arc who goes quiet is a different situation from a maintenance patient who skipped a month. The arc model gave us the context to tell the difference and respond accordingly.
The second thing that changed was how we identified our most valuable patients. MVPs at Chrystal Clinic weren't the patients who visited most frequently in absolute terms. They were the patients who had completed a full arc and converted to maintenance. That transition from acute or chronic treatment into an ongoing preventive relationship was the single most economically significant event in a patient's lifecycle with us. Getting a patient through her first arc wasn't just good clinical practice. It was the foundation of everything that came after.
The lifetime value gap between a patient who completed an arc and one who dropped out after two visits was not marginal. It was the difference that drove the $42,927 in year-one incremental revenue the economic model identified. The constraint was never acquisition. It was arc completion.
What it did for patient communication
Once we understood arc structure internally, we started communicating it externally. A patient who arrives understanding which arc she is on comes in with calibrated expectations. She knows roughly how many sessions to expect, what progress looks like at each stage, and what the natural endpoint is. That context changes her relationship to the early part of treatment, where most drop-off happens. She's not abandoning care when she feels slightly better after session two. She understands she's at session two of eight, not two of two.
We turned that into a sprint. The Find My Arc tool on the Chrystal Clinic website is the patient-facing output of the arc model: four questions, 60 seconds, a treatment arc with specific session counts, frequency guidance, and condition context delivered before the first appointment. It was built as a direct response to what the data showed about early drop-off. Patients were leaving not because the treatment wasn't working, but because they had no framework for understanding what working looked like over time.
This is what LUFT means by sprints. The diagnostic identifies the constraint. The sprint addresses it with a specific, bounded deliverable. Find My Arc is a retention sprint in patient education form. The analysis said visit-one-to-visit-two conversion was the largest revenue leak. The sprint built the tool that gives patients a reason to come back for visit two.
The question most clinics can't answer
Most clinic founders know their best patients by feel. They're the ones the front desk recognizes, the ones who rebook without prompting, the ones who refer friends. What most founders don't know is the economic weight of that cohort relative to everyone else: what share of total revenue they represent, what the path into that cohort looked like, and how many patients were one completed arc away from joining it and dropped out before they got there.
At Chrystal Clinic, patients who reached six or more visits generated 45.8% of all revenue and were worth 8.7 times more in lifetime value than patients who visited once. That cohort didn't happen by accident. They were patients who completed an arc, experienced a result, and built a relationship with the clinic that outlasted the original reason they came in.
The arc model made that visible. The sprint turned it into something the next patient could understand before she booked her first appointment.
You can try the Find My Arc tool at chrystalclinic.com/arc.
Luke Bujarski is the founder of LUFT and co-founder of Chrystal Clinic. LUFT builds economic models for cash-pay health clinics. luft.net