Put your clinic data to work.
Four engagements, four practice type, four models. Each began with a data export and ended with growth levers ranked by maximum impact. Figures are the clinic's own, not benchmarks.
Why the numbers mattered to us.
Feedback from independent clinic founders who have used LUFT analysis to identify retention, growth, and patient-lifecycle opportunities.
This was information I could not obtain from the standard reports in my EHR system. The analysis identified patterns in new patient visits and patient retention, along with suggestions on how to stabilize my business revenue using metrics I can track weekly.
Luke was incredibly helpful in identifying a decline in patient retention over the past few years that has affected our bottom line.
Several arc levers and actionable items were quickly identified and implemented, which have resulted in our patients investing more of their time and money in our clinic services.
Chrystal Clinic: $42,927 in year-one incremental revenue
A single-location integrative wellness clinic in Sycamore, IL. LUFT built the economic model, ranked five growth levers, and designed the operational playbook to pull them at zero incremental cost.
Five initiatives. Zero additional cost.
LUFT modelled five years of appointment data, ranked the growth levers by what each one returns, and translated the top five into specific operational actions. Here is what those actions are worth.
Pricing initiatives implemented March 2026 and showing no volume drop-off. Jane retention automations and MVP recruitment in active deployment. All figures are gross revenue from services only.
Five findings. The full data. Every action taken.
The complete report shows how each lever was identified, what it was worth, and the specific operational changes that captured it.
Fertility Acupuncture Practice: A conversion decline hiding inside flat revenue.
A fertility-focused acupuncture practice with revenue in the mid-$700Ks held flat for three years. LUFT identified the mechanism driving a steady multi-year decline in arc completion and quantified what restoring it is worth.
Three findings. A single cause.
Revenue looked stable. Underneath it, two measures had been moving against the practice for years, both tracing to the same mechanism.
Analysis based on more than 43,000 completed visits across 4,000+ patients over six years. Clinic identity withheld; identifying figures rounded. Recovery projections based on restoring first-arc completion to its earlier rate at current intake. Completer lifetime value derived from visit history. All analysis runs on de-identified data.
The decline was years old before it was visible.
Rising revenue per visit is a genuine operational strength. It also made a structural problem invisible for long enough that it compounded significantly before the model surfaced it.
Cross-clinic pattern recognition, applied to a fertility practice.
The finding was not in the revenue number. It was in knowing where to look, what to test, and what the pattern meant for the response.
Insurance-Mix Integrative Clinic: $100K+ in annual recoverable revenue, already in the building.
A multi-stream integrative practice with a roughly even split between insurance-billable and cash-pay services. LUFT analyzed the clinic's patient lifecycle data and quantified the revenue available from the existing patient base, at zero incremental acquisition cost.
Three findings. One week of analysis.
The clinic was already well-run. The engagement was scoped to find what was harder to see from the inside: the patient lifecycle patterns that don't surface in standard dashboards.
Analysis based on multi-year appointment export. Clinic identity withheld; identifying figures rounded. Revenue figures are directional; order-of-magnitude characterization based on diagnostic-layer data. All analysis runs on de-identified data; no patient information leaves the clinic's systems.
A well-run practice, with a gap the dashboards couldn't show.
The clinic was already tracking more than most practices its size. The work wasn't about finding obvious problems. It was about finding the ones that standard reporting can't surface.
From data export to delivered findings in one week.
One export from the practice management system. Three deliverables back.
Community Acupuncture Practice: A stable base with $150–230K in recoverable revenue.
A multi-modality practice running both community and private acupuncture, with a stable returning patient base and a quiet contraction running underneath it. LUFT identified three levers, grounded in five years of patient data, that address the contraction without adding a single new patient.
Three findings. One sequence.
The returning base was solid. The contraction was real. And the three levers that address it have to be worked in a specific order, because each one sets up the next.
Analysis based on more than 53,000 completed visits across 3,700+ new patients over five years. Clinic identity withheld; identifying figures rounded. Uplift figures are directional estimates grounded in the clinic's own LTV and arc completion data. Revenue figures are imputed from visit volume and average revenue per visit. All analysis runs on de-identified data.
Cross-clinic pattern recognition, applied to a dual-modality practice.
The findings were clear. The harder work was knowing what to do with them, and in what order.
Three questions, in one order.
The levers are sequenced because each one enables the next. Service mix first, then arc completion, then pricing.
What would the model find in yours?
LUFT works with a small number of independent clinics each quarter. It starts with one export from your practice management system.
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