Anonymized compositeHealthcare Hyderabad 90 days end-to-end

How a 34-Branch Diagnostic Chain in Hyderabad Cut No-Shows from 23% to 8%

A voice AI agent that handles reminder calls, rescheduling, and day-of-visit follow-up in Hindi, English, and Telugu — replacing a 12-person reminder desk that could only reach 40% of patients each day.

No-shows reduced

23% → 8%

65% improvement

Industry

Diagnostic Labs

Location

Hyderabad, Telangana

Timeline

90 days end-to-end

Client: A 34-branch diagnostic chain in Hyderabad serving ~1.2 lakh patients per month. Client name withheld. This page is an anonymized composite of recurring implementation patterns. Figures are representative internal examples, not independently audited results or guarantees.

Key Facts

Industry: Healthcare — Diagnostic Labs
Location: Hyderabad, Telangana, India
Problem: 23% no-show rate across 34 branches; ₹6–8 lakh/mo lost revenue
Solution: Multilingual voice AI agent for reminders and rescheduling (Hindi, English, Telugu)
Timeline: 90 days end-to-end
Headline outcome: No-shows dropped from 23% to 8% — a 65% improvement
Revenue recovery: ₹5.6 lakh/month preserved fasting-test revenue
Patient satisfaction: 4.6/5 on post-call survey (n=2,400)

The Challenge

▸23% no-show rate across diagnostic appointments, costing ₹6–8 lakh in lost revenue every month just from fasting-required tests.
▸Manual reminder desk of 12 callers could dial ~40% of scheduled patients per day; the rest got a single SMS that most patients ignored.
▸Regional-language barrier: ~55% of patients responded only in Telugu or Hindi, but the reminder desk was English-first.
▸Rescheduling required a callback to a busy landline, so patients frequently gave up instead of booking a new slot.

What We Built

1Deployed a multilingual voice AI agent (Hindi / English / Telugu) that auto-calls every patient 24 hours and 3 hours before their appointment.
2Built a rescheduling flow where the agent can offer 3 alternate slots conversationally and confirm the new booking end-to-end without human handoff.
3Integrated with the lab's existing LIMS and Bitrix CRM so all status changes reflect in operator dashboards in real time.
4WhatsApp fallback for patients who didn't pick up the first two calls — same intent, same rescheduling capability, same transcript.
5Compliance guardrails: PII minimisation, explicit consent capture, call recording with patient-aware disclosure per DPDP Act.

Representative Outcomes

These internal examples illustrate how success can be measured. Actual outcomes depend on baseline performance, data quality, integration scope, operating controls, and user adoption.

23% → 8%

No-show rate

65% reduction in 90 days

₹5.6 lakh/mo

Recovered revenue

Fasting-test appointments preserved

9 of 12

Reminder-desk staff redeployed

Re-assigned to high-touch patient care, no layoffs

4.6 / 5

Patient satisfaction on reminder experience

Post-call survey, n=2,400

Technologies Deployed

Voice AI (multilingual, Hindi/English/Telugu)
WhatsApp Business API
Custom integration with Bitrix CRM and internal LIMS
Sarvam AI speech recognition for regional languages

Services Used

Voice Ai Agents Ai Chatbots

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