Case Study: 40% Fewer No-Shows with AI Appointment Reminders

No-shows are the silent revenue drain of every healthcare provider. A missed appointment means a blocked slot, a wasted doctor's time, and lost revenue that can never be recovered. For a 10-doctor clinic running 80 appointments per day, a 25% no-show rate translates to ₹4–6 lakh in lost monthly revenue.

The Client

A multi-specialty clinic chain with 8 branches in Bangalore was experiencing a 28% no-show rate despite having a WhatsApp reminder workflow. The problem was that WhatsApp messages were being ignored — patients saw them but didn't take action to confirm or cancel. The clinic needed two-way confirmation, not one-way broadcasting.

The Implementation

Alphoris was configured to call every patient 24 hours and 2 hours before their appointment. The AI agent would confirm the appointment ('Hi, this is calling from [Clinic Name] to confirm your appointment with Dr. Sharma tomorrow at 10 AM — can you confirm you'll be there?') and log the response. Patients who confirmed were removed from the list. Patients who cancelled triggered an immediate slot-fill workflow.

Month 1 Results

No-show rate dropped from 28% to 19% — a 32% improvement in the first month. The clinic used the freed slots to accommodate walk-ins and same-day bookings, recovering an estimated ₹2.1 lakh in revenue that would otherwise have been lost.

Month 3 Results

After refining the call script and timing (moving the reminder from 24 hours to 36 hours ahead), the no-show rate stabilised at 16.5% — a 41% reduction from the baseline. The AI was handling 650 confirmation calls per day across all branches. The clinic estimated net revenue recovery of ₹5.2 lakh/month against a cost of ₹18,000/month in Alphoris credits — an ROI of 28x.

Lessons Learned

Voice confirmation outperforms text reminder by 3x for healthcare. Calling 36 hours ahead gives patients enough time to cancel and allows the clinic to fill the slot. A 'did you need to reschedule?' prompt at the end of confirmation calls recovers an additional 8% of would-have-been no-shows. Data from AI calls (cancellation reasons) helped the clinic identify that 40% of cancellations were due to transport issues — prompting them to partner with a local cab aggregator.