38.7%Aggregate Connection Rate
Education Technology
Context, Not Cold Scripts.
Read the case studyCase study · Healthcare & Hospitals
Voice AI for Hospitals: Every Call Carries the Patient's Context.
Hospitals run on high-volume, repeatable patient communication: confirming appointments, checking on discharged patients, fielding 'are my results ready?' calls, supporting palliative caregivers, chasing chronic disease adherence, following up on community screening referrals, and collecting feedback. Each task competes for limited clinical and administrative bandwidth, and each one only works if the call knows who the patient is. The Alchemyst context layer gives every call the patient's live record and history. Kathan Voice OS, running on top of it, absorbs the repeatable load, escalates anything clinically meaningful to the right human in real time, and runs natively in Indian languages including Tamil, Kannada, Telugu, and Hindi.
440K+Patients Served / Year (BBH)
Augment, Don't Replace.
01 · The problem
Communication Load Outstrips Clinical Bandwidth
Indian hospitals carry a communication footprint that no manual operation can keep pace with. Front desks spend hours on confirmation calls and still miss patients. Discharged patients return to rural districts with no structured follow-up; complications go undetected until they become readmissions. Lab portals only reach digitally literate patients, leaving a large rural and elderly cohort dependent on inbound calls. Palliative caregivers go days without a check-in. Community screening camps generate hundreds of referrals that are never acted upon. Paper feedback forms and SMS surveys collect single-digit response rates, so the patient experience signal arrives too late, if at all. The patient's context already lives in the HIMS. Nothing carries it to the conversation, and nothing brings the conversation back.
02 · The context layer
A Context Layer Wired Into HIMS
The context layer sits underneath existing hospital workflows rather than around them, and Kathan Voice OS speaks from it. Each call carries a live, filtered context retrieved from the HIMS in under 200ms: language preference, treating doctor, campus, procedure, medication list, palliative or chronic disease enrolment, no-show or referral history. Critical responses route to the right human in real time, with that context attached. Emergency keywords transfer directly to the doctor on call. Caregiver crises route to the palliative nurse. Unacted critical referrals route to the Community Health coordinator. Routine data flows back to the HIMS longitudinal record without a human touching it, so the next call starts from what this one learned.
- 170ms P50 context retrieval, sub-1-second voice pickup, and 500,000+ calls daily campaign capacity.
- Write-back to the HIMS longitudinal record on every call, so the patient's history compounds instead of resetting.
- Three escalation modes: full automation, AI-first with human close, and AI-assist for clinically sensitive moments, each handing the full patient context to the clinician.
- Native multilingual coverage: Tamil, Kannada, Telugu, Hindi, Malayalam, English, and 7+ more Indian languages, synthesised natively, not dubbed.
- TRAI-compliant caller ID, time-window enforcement, and complete audit trails on every patient interaction.
03 · Where it fits
Where It Fits in the Patient Journey
Seven workflow gaps repeat across nearly every multi-specialty and mission hospital we have scoped. Each one is high-volume, repeatable, depends on knowing the patient, and is currently absorbed by clinical or administrative staff who would be better deployed elsewhere.
01OPD Appointment Management
priority voice for high-risk no-show patients, WhatsApp for the rest, live reschedule against doctor availability, three-deep waitlist engine on cancellations.
02Post-Discharge Well-Being Check-Ins
48-hour structured recovery assessment against the actual procedure and medication list, with severe pain or warning symptoms escalating directly to the treating physician.
03Test Report Ready Notifications
HIMS lab webhook triggers identity-verified outbound calls; routine results delivered as time-limited PDF, abnormal results auto-book a consultation.
04Palliative & Hospice Family Support
scheduled caregiver check-ins covering patient comfort, medication supply, and caregiver wellbeing, with critical flags routed to the palliative nurse on call.
05Chronic Disease & Dialysis Adherence
condition-matched outreach (post-dialysis the next day, fortnightly for hypertension and diabetes) with same-day care team alerts on missed sessions or out-of-range readings.
06Community Health Outreach Callbacks
post-camp follow-up in the patient's village language, repeating the referral by name, with unacted critical referrals routed to a coordinator.
07Patient Experience Feedback
24 to 48 hour post-visit NPS calls with department-specific probes; detractors escalate to Patient Relations within four hours, promoters get a Google review nudge.
04 · Why it works
Why It Works for Healthcare Specifically
Voice closes the channel gap that SMS and patient portals cannot. A meaningful share of any Indian hospital's patient base is rural, elderly, or low-literacy and never opens a portal link. A voice call in Kannada or Tamil reaches them on the first ring. The context layer is what makes that call worth answering: every call carries the actual procedure, medication, and treating doctor, so the agent references real specifics rather than a generic template, any concerning response reaches a named clinician within minutes rather than days, and every answer is written back so the next call, and the next clinician, picks up where this one left off. The result is a system that augments existing nursing and front desk teams instead of asking patients to adapt to a new channel.
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Every conversation should start from what you already know.
Tell us which systems hold your customer's history and which workflows keep starting from zero. We will show you where the context layer slots in, without replacing what already works.