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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It 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 target patients with 60–70% no-show probability — 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. It 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, following each patient's EHR preference; high-risk patients without one are sequenced SMS → email → IVR. 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, with provider and room rules 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 in 15–30 minutes moves to the next, and confirmed bookings write to the EHR instantly. Most practices recover 60–75% of slots arising 2+ hours out.
EHR: Epic (FHIR R4, MyChart, ADT), Oracle Health/Cerner, athenahealth, eClinicalWorks, NextGen, Allscripts/Veradigm, and Kareo/Tebra. Legacy PMS without APIs are supported via CSV or HL7 extract. Communication via Twilio, SendGrid, Klara, Phreesia; transportation via Lyft Health, Uber Health, and Modivcare.
Messages contain only first name, appointment date/time, and practice name — no diagnosis or PHI, with prep instructions behind an authenticated link requiring date-of-birth confirmation. Data is stored with AES-256 encryption at rest and in transit, audit logging, role-based access, and a signed BAA before deployment.
Standard implementation is 4–6 weeks: EHR integration, risk model calibration and outreach config, then parallel run and go-live. For a 4-provider practice at 15% no-show and $250 average value, expect a drop to 9–10% by week 12 — recovering 8–10 slots/day and $180K–$225K annualised.
Standard automated appointment reminders send the same message to every patient on a fixed schedule. This no show prevention software agent first scores each appointment for risk using 30+ signals, then matches outreach intensity to that risk — low-risk patients get one confirmation, high-risk patients get early, multi-channel, barrier-specific patient scheduling reminders. That is what drives a sustained 40–50% reduction in no-show rates.
Yes. The appointment reminder software delivers automated appointment reminders across SMS, email, IVR voice, patient portal, and push, with the channel mix set per risk tier and each patient's EHR preference. Every patient scheduling reminder carries a one-touch confirm or reschedule option, and all outreach is HIPAA-compliant with details behind an authenticated link.
