Healthcare · Appointment no-show prediction
Banao builds appointment no-show prediction models that score every booked patient for cancellation risk before the slot is lost — and route the right intervention automatically.
SMS nudges, phone callbacks, or waitlist promotion: the model picks the action, your scheduling team sees the queue. Empty slots shrink. The model runs on your existing booking data, not a generic dataset.
Prediction without intervention is a report. We build the full loop: score, route, act, and measure.
Every booking gets a no-show probability the moment it is created — before the reminder window. High-risk appointments enter the intervention queue immediately, not 24 hours before.
Prior no-show history, travel distance, appointment type, day and time, days since last visit, insurance type, and booking channel — we engineer the features that actually predict in your population, not a generic list.
High-risk patients get a phone call. Medium-risk get an SMS series. Low-risk get nothing extra. The routing rules are yours to define; the model assigns the lane.
When a cancellation is predicted or confirmed early enough, a waitlist patient is offered the slot automatically — filling the gap before the clinician's schedule is published.
Tomorrow's high-risk appointments, intervention status per patient, and weekly no-show rate by clinic and appointment type — one view for scheduling coordinators.
Patient populations shift. We monitor prediction accuracy week-on-week and trigger retraining when calibration moves — so performance holds over months, not just the first quarter.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Hummcare
Banao built the Hummcare platform end to end, including appointment scheduling and patient communication. No-show risk scoring was woven into the booking flow from the first production release, feeding the intervention and waitlist-fill pipeline.
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens our own engineering hires each week; Vikaas runs our own demand-generation pipeline. A model that has to perform inside our own operation is already battle-tested before it reaches your scheduling system.
We are not describing production AI from the outside. We depend on it — which is the standard we apply to every prediction model we build for a client.
Not every clinic needs an AI model. We will tell you before you commission one:
We do not quote a prediction model off a slide deck. We look at your actual booking history first.
We audit your last 12 months of booking and attendance data, engineer candidate features, and run a baseline model. You get an accuracy estimate, a no-show breakdown by segment, and an ROI model — yours to keep. If you proceed, the Sprint is credited against the build.
Train to your population, integrate with your scheduling system (Epic, Cerner, bespoke), wire the intervention routing to your SMS and call-centre tooling, and deploy the waitlist-fill automation.
Live dashboard for scheduling teams, weekly accuracy reports, drift detection, and a retraining protocol. We do not hand over a model and walk away.
What booking data does the model need?
At minimum: appointment datetime, appointment type, patient ID, and attended/no-showed outcome for the last 12 months. Additional signals — travel distance, insurance, booking channel, prior history — improve accuracy, and we pull them from your existing systems during the Discovery Sprint.
Does it integrate with Epic, Cerner, or our bespoke system?
Yes. Banao has integrated with Epic, Cerner, and multiple proprietary scheduling systems via HL7 FHIR, API, and direct database read. Integration approach is confirmed in the Discovery Sprint and is part of the build deliverable, not a separate project.
How does the model decide which patients need a call versus an SMS?
Routing rules are yours to define — typically by risk band and cost of intervention. The model assigns patients to a risk band; your rules map bands to actions. You can change any routing rule without retraining the model.
How quickly can a waitlist patient fill a cancelled slot?
Waitlist patients can be offered a slot within minutes of a predicted or confirmed cancellation, depending on your scheduling system's write API. The Discovery Sprint confirms the realistic fill window for your setup.
How long before we see a measurable reduction in no-shows?
Most clinics see directional movement in the first four to six weeks post-deployment, once the intervention pipeline is warm. Statistical confidence typically takes one full cycle — around three months. The model improves from there as it accumulates more outcome data.
Bring your appointment volume and your current no-show rate. In 45 minutes we will tell you what the model opportunity is, what your booking data supports, and what the recoverable capacity looks like.
Book a 45-min scoping call