Patient no-show software that predicts at-risk patients, triggers multi-channel reminders, fills cancellations from waitlists, and recovers lost appointment revenue.
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No-shows cost US healthcare $150B/year — 18–22% in primary care, up to 50% in behavioural health. A 4-provider practice at 15% no-show rate loses $1.3M–$2.2M annually. Most are preventable: 41% from forgetting, 23% from scheduling conflicts.
Most practices send one reminder to every patient — same channel, same timing, regardless of history. Bonami's agent uses AI risk scoring to match outreach intensity to each patient's actual no-show risk.
When engagement drops — no response to reminders or negative sentiment detected — the agent triggers self-service rescheduling before the slot is lost, turning likely no-shows into confirmed future bookings.
When a cancellation occurs, the waitlist engine instantly contacts the highest-priority waiting patient — recovering 60–75% of vacancies that arise 2+ hours before the appointment.
Six capability pillars across primary care, specialist, dental, behavioural health, and large health systems.
Six capability pillars across primary care, specialist, dental, behavioural health, and large health systems.
ML model scores every appointment on 30+ signals — no-show history, lead time, type, time of day, insurance, and distance. Trained on 6–12 months of practice data.
Outreach via SMS, email, IVR, patient portal, and push — channel mix set per risk tier and patient preference. Each message includes a one-touch confirm or reschedule option.
Patients reply "reschedule" to any message and pick from available slots via a HIPAA-compliant, date-of-birth-verified flow — under 60 seconds on mobile.
On cancellation, the agent ranks waiting patients by urgency, wait time, and proximity — first to confirm gets the slot, booked in the EHR within seconds.
Transportation barriers trigger automatic NEMT coordination (Lyft Health, Uber Health, Modivcare) with ride confirmation sent ahead. SDOH flags elevate risk scores and route patients to care coordination.
Native connectors for Epic, Oracle Health/Cerner, athenahealth, eClinicalWorks, NextGen, Allscripts, and Kareo/Tebra. Reads schedules, writes status, and logs communications as appointment notes — no manual steps.
A 4-provider practice at 15% no-show loses $1.3M–$2.2M/year — from patients who forgot, couldn't reschedule, or hit an unaddressed barrier. Bonami's agent recovers 40–50% of that loss within 90 days.
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An AI Appointment No-Show Prevention Agent reads your EHR schedule, predicts at-risk patients, deploys personalised multi-channel outreach proportional to risk, enables self-service rescheduling, and fills vacancies from an automated waitlist. Unlike standard reminders, it uses ML to identify patients with 60–70% no-show probability and targets outreach accordingly — delivering a sustained 40–50% reduction in no-show rates.
The risk model uses 30+ signals from EHR data, demographics, and engagement history. Prior no-show history is the strongest predictor — a repeat no-show patient carries 3–5x higher probability. Appointments booked 21+ days out and behavioural health visits no-show at 2–3x the primary care rate. The model calibrates on 6–12 months of practice data over 4–6 weeks, then refines continuously from every appointment outcome.
Channels: SMS, email, IVR, patient portal, and push. Mix follows the patient's EHR preference; for high-risk patients without one, the agent sequences SMS → email (no 24h response) → IVR (within 48h of appointment). All outreach is HIPAA-compliant — appointment details sit behind an authenticated link, not in the message body.
The patient verifies date of birth, picks from the next 3–5 available slots, and confirms in one tap — under 60 seconds on mobile. The original slot releases for waitlist backfill and the new appointment is written to the EHR in the same transaction. Appointment type constraints, provider rules, and room requirements are enforced automatically.
The engine maintains a real-time priority list by appointment type, provider, insurance, and clinical urgency. On vacancy, the top-ranked patient is contacted immediately; no response within 15–30 minutes moves to the next. Once confirmed, the booking is written to the EHR instantly. Most practices recover 60–75% of slots arising 2+ hours before the appointment.
EHR: Epic (FHIR R4, MyChart, ADT), Oracle Health/Cerner, athenahealth, eClinicalWorks, NextGen, Allscripts/Veradigm, and Kareo/Tebra. Legacy PMS platforms without APIs are supported via CSV or HL7 extract. Communication: Twilio, SendGrid, Klara, Phreesia. Transportation: Lyft Health, Uber Health, Modivcare.
Messages contain only first name, appointment date/time, and practice name — no diagnosis or PHI. Clinical prep instructions are behind an authenticated link requiring date-of-birth confirmation. Patient data is stored with AES-256 encryption at rest and in transit, audit logging, role-based access controls, and a signed BAA executed before deployment.
Standard implementation: 4–6 weeks. Weeks 1–2: EHR integration and data extraction. Weeks 3–4: risk model calibration and outreach config. Weeks 5–6: parallel run and go-live. For a 4-provider practice at 15% no-show and $250 average value: expect rate reduction to 9–10% by week 12 — recovering 8–10 slots/day and $180K–$225K annualised.
