What Happens at 30 Cases
The engine's compounding value doesn't show up on the first visit — it shows up at the cohort level. This page takes 30 synthetic patient cases, runs each through the engine, and then runs real in-browser machine learning (k-means clustering + a decision-tree risk classifier) against the outputs — to show what the clinic learns once it has a month of data to look at.
How This Works
The cohort below is 30 synthetic patient cases spanning six realistic archetypes (overdue seniors, geriatric patients with symptoms, mid-life breed-predisposed, young acute presentations, puppy/routine wellness, and recurrent-issue patients). Each was processed through the engine's recommendation logic, then passed through a realistic acceptance simulation that accounts for client type (price-sensitive, "pet seems fine," engaged) and rec priority.
Everything from the charts down is computed client-side in your browser from the cohort JSON — the k-means clusters and the decision tree are trained every page load. No server, no hidden backend. Inspect cohort.js to read the ML code.
1. Cohort Composition
Cohort of 30 patients. The engine processes a realistic mix — this is what 30 routine visits at the clinic looks like.
Patient archetype distribution
Breakdown
Overall risk level distribution
The engine assigns an overall risk level to each patient based on signalment, visit gap, and presenting signs. This is what the engine flagged across the cohort.
2. K-Means Clustering (k=4)
Each patient is represented as a 9-dimensional feature vector (age, weight, visit gap, number of recommendations, projected revenue, symptomatic flag, senior flag, overdue flag, species). K-means finds four natural groupings. The scatter plot below projects those into the two most interpretable axes — age and visit-gap — but the clustering uses all nine dimensions.
Auto-derived cluster labels
Labels above come from each cluster's dominant features. The engine is picking up on patterns no single visit would reveal.
3. Risk Classifier (Decision Tree)
A shallow decision tree (max depth 4, information-gain splits) trained on the same 9-dimensional feature vectors. It learns to predict the engine's risk assignment from signalment alone — which means once trained, it can score new patients instantly, no API call needed. The tree structure below is the real trained tree.
Production note: A real deployment would train this on hundreds of the clinic's own accepted/declined cases with cross-validation. At 30 synthetic samples, the accuracy number is indicative — not a rigorous holdout metric. The value here is demonstrating that the engine's recommendations can be distilled into a fast, explainable model the clinic owns and inspects.
4. Revenue Translation
The dollar math on this cohort, and what it scales to annually. Baseline capture of 16% is the industry figure cited in the CEO deck (dvm360); the engine-guided capture rate is what the acceptance simulation produced across the 30 cases.
How the annualized number is computed: per-visit uplift on this cohort × (4,200 annual wellness visits × 40% flagged share) = annual projection. The 40% flagged share is the deck's assumption on Slide 3 — only visits the engine flags get the full lift, not all 4,200 visits.
5. Key Insights
The operational takeaways — where the capture gap is concentrated, which clients decline for which reasons, and which breed/age combinations are under-recommended. These are the patterns the clinic would act on in the next planning cycle.
Capture rate by visit-gap cohort
Clients overdue 12+ months convert differently than clients seen in the last 6. This is where the clinic's reminder-timing strategy lives.
Decline reasons — where the revenue leaks
Of the recommended diagnostics that didn't convert, these are the reasons. The distribution tells the marketing team which framing to lead with: cost-framing, medical-necessity framing, or urgency framing.
By revenue at risk
Worst-capture breed × age cells
Where the clinic's capture rate is lowest — ranked by volume-weighted impact. These are the cells that would benefit most from engine-surfaced prompts and clinician-side decision support.
| Breed / age band | Recommended | Captured | Rate |
|---|
6. All 30 Cases
Every patient in the cohort, their engine-assigned cluster, and their individual economics.
| # | Patient | Age | Gap | Risk | Cluster | Recommended | Captured |
|---|
From Demo to Deployment
At 30 cases, patterns are already emerging. At 3,000 cases — one month of live clinic operation — the clustering becomes predictive, the risk classifier retrains on real acceptance data, and the engine stops being a prompt generator and starts being the clinic's most experienced staff member.
See the Full Rollout Plan