How to read a waitlist recovery case study
Use the published result as a question generator, not as a promise. A useful case study makes the starting condition, workflow, period and denominator visible.
| Claim to inspect | What to request | Why it matters |
|---|---|---|
| Slots filled | Eligible openings, offers sent, claimed slots and attended visits | A claimed slot is not automatically attended care |
| Revenue recovered | Collected amount, service mix, time window and attribution rule | Scheduled value and collected revenue are different |
| Speed to refill | Median and distribution from cancellation to confirmed replacement | One record time can hide the normal experience |
| Patient experience | Opt-outs, failed claims, complaints and preference controls | A full calendar should not come from indiscriminate messaging |
| Staff time | Manual calls before and exception work after | Automation can shift work instead of removing it |
Published examples, with the source visible
Norbo Dental — vendor-reported recovered revenue
NexHealth reports that Norbo Dental filled 15 slots per week and attributed $3,000 in weekly revenue to its Waitlist workflow. This is a named vendor case study, not an independent controlled study or a YellowHorns result.
Read the source: NexHealth case study ↗Grand Street Dental — overall patient volume
NexHealth reports a 22% increase in overall patient volume alongside online booking, reminders, messaging and an ASAP list. Because several features changed, the published page does not isolate the waitlist's individual contribution.
Read the source: NexHealth case study ↗What do the published examples show?
The strongest named example found in the reviewed sources is Norbo Dental. NexHealth says the practice previously called patients manually, then used its waitlist product to let patients claim appointments by text or email. The page reports 15 filled slots and $3,000 in weekly revenue. Those figures are useful because the practice and operating change are named, but they remain vendor-published.
A second NexHealth case study reports that Grand Street Dental used online booking, reminders, messaging, marketing campaigns and an ASAP list, with a 157% increase in online appointment requests and 22% increase in overall patient volume. That is broader patient-experience evidence; it should not be presented as proof that the waitlist alone caused the full result.
What should a private practice measure?
Start with open slots created by cancellation, the percentage eligible for recovery, patients offered the slot, confirmed replacements and actual attendance. Show counts beside rates. Record the time from opening to confirmation and the time staff spent managing exceptions.
Keep the cancelled patient's rebooking separate from filling the newly open slot. Both matter, but they are different journeys. Report collected revenue only after defining the attribution window and excluding appointments that would have booked without the recovery workflow.
What makes the workflow patient-friendly?
Use stated preferences, appointment type, provider, location and realistic travel time. Make the offer easy to accept or decline, stop after the slot is taken and avoid repeatedly notifying people who did not ask for earlier availability. Preserve opt-outs and give staff a visible way to resolve conflicts.
A fast fill is not successful if several patients believe they secured the same slot. The system needs atomic booking or an authoritative final check, idempotent event handling, expiration logic and a clear message when availability changes.
How should a practice pilot waitlist recovery?
Choose one appointment type with recurring cancellations and enough demand to refill it. Document the baseline for four to eight weeks, define eligibility and create a small synthetic test set before contacting real patients. Verify confirmations, cancellations, reschedules, duplicate records and calendar failures.
Review the pilot with the front desk and practitioners. Expand when the process produces attended replacements with manageable exception work and acceptable patient feedback—not simply because messages were delivered.
What this definition cannot tell you
The outcomes above are vendor-reported and are not YellowHorns client results or promises. The two examples come from one vendor, and one combines multiple product changes. Results depend on demand, capacity, service value, data quality, booking rules and patient choice.