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No-Show Prediction

Hour-by-hour no-show prediction with risk scoring.

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What this agent does

No-Show Prediction is the agent that evaluates the probability that a patient will not attend their next appointment. It combines a gradient boosting model trained on the clinic’s real history with contextual signals (day, hour, distance, weather, booking lead time). The goal is clear: reduce no-shows by up to 30% by enabling targeted confirmation of at-risk cases.

Why it matters

A clinic with 15-20% no-show rate loses revenue and blocks slots from other patients. Calling every patient 48h in advance is operationally expensive. No-Show Prediction sorts by risk: reception only needs to focus the confirmation effort on the top 20%, not on 100%.

How it works

The agent runs an hourly scan over appointments in the next 72 hours. For each one it computes a 0-100 score and classifies it as LOW/MEDIUM/HIGH. HIGH triggers a notification to reception with action suggestion (WhatsApp / call / SMS based on patient preference). 24h idempotency per appointment prevents duplicates between ticks.

Integration with the clinical workflow

No-Show Prediction consumes from the appointments module and the patient history. The generated notification arrives at Reception, which handles it with the appropriate confirmation template. When a HIGH appointment is cancelled after confirmation, it releases the slot to the manager for manual reassignment or via the waitlist.

Autonomous decisions it makes

Recompute the score of each appointment on the hourly scan
Classify each appointment as LOW/MEDIUM/HIGH per calibrated thresholds
Generate notification to reception only for HIGH appointments
Suggest contact channel based on the patient's declared preference
Avoid duplicate notifications with 24h idempotency

Inputs and outputs

Receives

· Patient's past appointment history (attendance and no-shows)
· Day of week, hour and booking lead time
· Distance between home and clinic (optional)
· Expected weather data for the day (optional)

Produces

· 0-100 score per appointment
· LOW / MEDIUM / HIGH classification
· Notification to reception with action suggestion
· Prediction traceability on the appointment

Production metrics

-30%

No-show reduction

Hourly

Scan

24h per appointment

Idempotency

Collaborates with

Reception

Patient Flow Forecast

Patient Segmentation

Tech stack

Model

Gradient boosting trained on clinic data

Execution

Local CPU (lightweight model, no GPU required)

Latency

Hourly scan over 72h window

Privacy

Model trained with data from the clinic only

Frequently asked questions

Is the model trained with data from other clinics?+

Not by default. Each clinic has its own model trained only on its history. This avoids biases from different populations and maintains data sovereignty. Optionally, anonymized data can be mixed between clinics of the same group if signed off.

What if a new patient has no history?+

The model applies a score based on contextual signals (day, hour, lead time, distance) and returns a lower confidence level. Reception can treat these patients with the standard first-visit protocol.

How is the 30% reduction measured in my clinic?+

We compare no-show rate 3 months before and 3 months after deployment, on the same appointment perimeter. The manager dashboard shows the comparison live.

Can the prediction be seen by the patient?+

No. The score is internal and only shown to reception and manager. The patient never sees that they were classified as HIGH. The visible action for them is the usual confirmation.