Solution Database / Healthcare
Recall List Recovery
Overdue patient recall lists are long, full of dead numbers, and rarely get fully worked through, so clinics miss care and revenue. The suppression list and human-first opt-out are built into every message, so the AI never contacts a bereaved family or a patient who has moved clinic.
01The offer
For practice managers at private dental and physiotherapy clinics, turn the overdue recall list from the practice management system into booked appointments and a clean daily exception queue. Address the recurring problem: recall lists are skimmed, not worked, so patients drift away and unbilled appointments pile up. The value hypothesis is a measurable increase in recall conversion with minimal staff effort; the pilot must establish whether that benefit is real.
- For
- Practice managers at private dental and physiotherapy clinics
- Takes in
- Overdue recall list, patient contact preferences, live appointment calendar, suppression list
- Delivers
- Booked appointments, daily staff summary, flagged conversation log
- Message
- Turn your overdue recall list into booked appointments while your team sleeps.
- Lead magnet
- A free two-week trial on one appointment type, showing the conversion rate on your own recall list.
02How it works
- Pull overdue patients nightly from the practice management system
- Check contact history and suppression list before any outreach
- Choose channel by preference and bounce history
- Send personalised SMS, email or AI voice call with live slot offers
- Handle replies conversationally and propose two real slots
- Generate a daily summary for staff sign-off
Workflow
Pull overdue list, filter suppression list, select channel, send personalised message, handle replies with AI, propose and book slots, and review daily summary. Start with overdue recall list and contact preferences and finish with booked appointments and a clean exception queue.
AI and people
Use language models to draft plain-language messages and run a conversational voice or text agent that answers simple questions and proposes slots. A deterministic scheduler checks recall dates and suppression flags. A human receptionist reviews every booking and any flagged reply before it is committed.
Screens
Key screens: Recall dashboard, patient outreach queue, booking confirmation log. Use a dashboard showing overdue counts by type and channel, a queue of patients with contact preference and suppression flags, and a log of AI conversations and bookings. Let staff filter by appointment type and date. Display outreach status: sent, replied, booked, declined, flagged. Provide a single-click approval for AI-proposed bookings. In this product, the first view is recall dashboard, followed by patient outreach queue and booking confirmation log.
Admin
Clinic-level accounts, staff roles for review and sign-off, message templates, suppression list management, booking audit trail, opt-out records and a full conversation log for compliance.
03Market gap
Alternatives buyers use today
Receptionists manually calling down a printed recall list on a quiet Friday. This differs by working the whole list overnight, across channels, and booking straight into the calendar.
Where this wins
The more clinics use it, the better the AI gets at phrasing, timing and handling edge cases, and the suppression and compliance rules become a benchmark competitors cannot match without the same accumulated data.
04Why now
Healthcare teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: overdue patient recall lists are long, full of dead numbers, and rarely get fully worked through, so clinics miss care and revenue.
05Proof & signals
Channels where buyers gather: Dental and physio practice manager forums, industry conferences, and direct outreach to clinic owners via LinkedIn and email.. Metrics that prove it works: Recall list conversion rate and number of bookings made without staff phone calls..
Paid pilot
A four-week paid pilot at one clinic proves it by comparing the conversion rate of the AI-worked list against the prior quarter's manual recall conversion. The decision to continue is based on at least a 20% increase in booked recall appointments with no compliance incidents.
06Execution plan
MVP
One dental clinic, one appointment type such as six-month check, SMS and email only, fixed slot set, and manual review of every booking before commit.
First 30 days
Week 1: Connect to one practice management system and build the recall trigger. Week 2: Build the suppression list and message templates. Week 3: Run SMS and email outreach with manual booking review. Week 4: Test with the pilot clinic and refine the daily summary.
After the pilot
Add AI voice calls, multiple appointment types, automatic follow-up scheduling for declined patients, and direct booking into the live calendar without manual review for low-risk cases.
Retention
The system keeps working nightly with no staff effort, and each month it learns better phrasing and timing, so the clinic sees compounding recall revenue and stays because the alternative is a guilt pile again.
Integrations
Practice management systems such as Dentrix or Exact, SMS and email providers, and the clinic's live appointment calendar.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case. Manual review in the loop. | 5 days | $8,500 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 6 days | $10,000 |
| Full product | Self-serve onboarding, billing, monitoring and the wider integration set. | 10 days | $14,000 |
| Total | $32,500 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $50–$100 | $40–$90 | $90–$190 |
| Full product (about 50 customers) | $190–$380 | $280–$560 | $470–$940 |
Revenue model to test
Test at USD 300 per clinic per month, plus a small per-booking fee of USD 2, as a hypothesis.
Cost drivers
Practice management system integration, AI conversation minutes, SMS and email sending, and support time for setup and troubleshooting.
Safeguards
Strict suppression list enforced before any contact, opt-out on every message, human review of all bookings and flagged replies, role-based permissions, and a rule that the AI must never contact a patient on a suppression list or make a booking without staff approval in the pilot phase.
Take it further
Solution blueprint, rewritten from an earlier Nexibeo concept. Demand, pricing, build scope and integrations are working assumptions, not verified market findings. The MVP and the paid pilot exist to confirm them for your business before the larger build.