US dental missed-call leakage diagnostic example
A transparent synthetic proof-of-work showing how AICloudStrategist measures missed-call recovery before recommending AI receptionist, callback or CRM workflow automation.
Honesty label
This is a simulated internal proof-of-work asset. It uses synthetic call-log rows only. It is not a real customer case study, not a testimonial, not a HIPAA compliance claim, and not evidence that any dental practice achieved bookings, revenue, ranking, patient growth, or operational results.
Why this asset exists
AICS has a buyer-ready US dental missed-call diagnostic package. This proof page shows the measurable logic behind that offer: identify missed-call leakage, source attribution gaps, callback SLA weakness and owner-visible evidence before proposing automation.
Synthetic input summary
| Metric | Result from synthetic sample | Diagnostic meaning |
|---|---|---|
| Business-day/channel rows | 10 | Small sample for demonstrating method, not a real practice export. |
| Total missed calls modelled | 76 | Leakage pool that needs callback ownership and source visibility. |
| Callback attempts | 39 / 76 (51.3%) | Nearly half of missed calls have no callback attempt in the synthetic baseline. |
| Same-day callbacks | 22 / 76 (28.9%) | Same-day response SLA is visible and can be improved operationally. |
| No callback recorded | 37 | Owner dashboard should show unresolved missed-call volume. |
| Booked visits from callbacks | 17 | Measured from callbacks in the synthetic sample; not a promised outcome. |
Leakage by source channel
| Channel | Missed calls | Callback attempts | Callback coverage | Booked from callback |
|---|---|---|---|---|
| Google Ads | 24 | 12 | 50.0% | 5 |
| Google Business Profile | 30 | 16 | 53.3% | 7 |
| Insurance directory | 9 | 4 | 44.4% | 2 |
| Website call button | 13 | 7 | 53.8% | 3 |
Before and after diagnostic example
This is a modelling example, not a promised outcome. It keeps the observed booking rate on callback attempts unchanged and only models better operational coverage.
| Metric | Before: synthetic baseline | After: diagnostic SLA target |
|---|---|---|
| Callback coverage | 51.3% | 90.0% |
| Same-day callback coverage | 28.9% | 75.0% |
| Callback attempts | 39 | 68 |
| Same-day callbacks | 22 | 57 |
| Incremental callback attempts to review | — | 29 |
| Modelled incremental booked visits if observed callback booking rate holds | — | 12.6 |
| Modelled first-visit value at weighted synthetic value | — | $2,329 |
Owner-dashboard proof pack
- 14–30 days of phone-system missed-call exports by date, time and source where available.
- Callback timestamps and disposition labels: reached, voicemail, booked, not interested, duplicate, existing patient or spam.
- Appointment-booking counts by lead source, using aggregate or privacy-safe exports only.
- Front-desk schedule and callback ownership rules.
- Screenshots of current call tracking, CRM, form, Google Business Profile and voicemail workflows.
- Optional de-identified sample of five missed-call journeys for qualitative bottleneck review.
- Written approval before any public case study, logo, testimonial, or outcome claim.
Reproducible artifact
The source model and generated markdown report are stored internally at /home/agent/.hermes/aicloudstrategist/case-studies/simulated-us-dental-missed-call-leakage-2026-07-11/. Inputs are explicit in sample_call_log.csv; calculations are deterministic and labelled as simulated.
Verification: python3 dental_missed_call_diagnostic.py regenerated the report with rows=10, missed_calls=76, callback_attempts=39, callback_coverage=0.513, same_day_coverage=0.289, booked_from_callbacks=17, incremental_callbacks_to_90pct=29 and modeled_first_visit_value_usd=2329.44. Input SHA256: 085bb1f63a099198932dbcd4d5ce50da6adde966dcc86a3ddab3de5f73617451.
Claim boundary
No real clinic, patient, PHI, logo, testimonial, HIPAA compliance, legal advice, medical advice, security certification, patient outcome, booking, ranking, revenue, callback-rate or AI-receptionist accuracy guarantee is claimed. This means no legal advice, no medical advice, no booking guarantee and no revenue guarantee. Real implementation would require explicit scope, data-handling approval, payment route and written authorization before any public proof use.