A 4-dentist practice was losing $340K annually to no-shows and lapsed patients. Front desk spent 3 hours daily on reminder calls. The Aizen Event automated the entire patient communication workflow: scheduling, reminders, and recall: without replacing a single staff member.
7 steps, 3.5 hours per day of phone calls, 28% no-show rate. Staff spent most of their time on manual outreach with minimal data about which patients were at risk.
Each step mapped to an AI type: Deterministic (rules-driven scheduling and messaging), Probabilistic (risk scoring for reminders), or Human (clinical judgment untouched). This drives what to automate first.
| Step | AI type | Recommendation | New state | Time saved | |
|---|---|---|---|---|---|
| 01 | Appointment booking Manual call entry + PMS lookup |
Deterministic | Online self-service portal with real-time PMS sync. Automates ~65% of bookings. Remaining 35% (complex cases) stay with front desk for assisted booking. | ● Augmented | 5 min |
| 02 | Appointment confirmation Manual calls, no tracking |
Deterministic | Automate 3-touch sequence: 1 week prior (email), 48 hrs (SMS), 24 hrs (voice). Confirmation tracked automatically. Unconfirmed flagged for staff callback. | ● Automated | 4 min |
| 03 | Reminder calls Gut-feel risk selection, manual calls |
Probabilistic | Risk-scored reminders: patients with prior no-shows get extra touch. Personalized with appointment details and provider name. Fully automated. | ● Automated | 3 min |
| 04 | No-show management Manual rescheduling, low fill rate |
Deterministic | When no-show flagged: immediate SMS with reschedule link + automated waitlist check. Auto-fills ~34% of same-day slots. | ● Automated | previously reactive |
| 05 | Post-visit follow-up No systematic outreach |
Deterministic | 48hr post-visit: treatment plan reminder + care instructions. 30-day: satisfaction survey. Flags negative sentiment for provider review. | ● Automated | previously zero |
| 06 | Recall outreach Manual monthly process, low contact |
Deterministic | Monthly PMS query identifies overdue patients. Personalized multi-touch: email → SMS → postcard. Smart send-time optimization. Fully automated. | ● Automated | 4 hrs/month |
| 07 | Waitlist management Manual list, ad-hoc calls |
Deterministic | Digital waitlist with instant SMS when slot opens. Patient one-tap confirms. Auto-fills ~80% of cancellations within 2 hours. | ● Automated | ad-hoc → systematic |
We plot each step by implementation complexity (X) against business impact (Y). Steps in the top-left are Quick Wins: start here. Top-right are Strategic Investments: build after wins are live.
3-touch sequence. Highest impact on no-shows. Built in week 1.
Probabilistic scoring. Low complexity, high ROI. Week 1–2.
Digital queue + SMS. Fills slots instantly. Week 2.
Monthly automation. Eliminates 4 hrs of manual work.
Auto-fill same-day slots. Quick Win border.
65% reduction in call load. Phase 2.
Satisfaction + retention. Longer play, Phase 3.
The redesigned workflow after the Aizen Event implementation. Every step now has a clear new state showing before, after, and the impact on staff time.
Build cost, deployment timeline, and three-year return: based on observed patient volume, staff loaded costs, and measured clinic outcomes post-deployment.
This case study represents a composite of typical SMB dental practice engagements. Metrics are based on observed industry benchmarks and comparable clinic implementations. All investment figures are illustrative ranges based on the Otonmi service tiers.
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