Patient intake in Hindi and English
Intake for a multi-specialty hospital in Bengaluru. Patients call once, in whatever language they speak, and a structured intake note is posted to the EHR over an HTTP tool before they reach the front desk.
Multilingual AI agents for patient intake, scheduling, reminders, and post-visit follow-up. Voice and video, in the patient’s own language. Clinical handover in one tool call, pointed at your on-call system. Live in fifteen minutes.
Receptionists are saints, but they’re outnumbered. Phones ring, no-shows pile up, and the third call from the same patient ends up in a different language than the first two.
Patient-first warmth, clinical constraints written into the prompt, and a clean handoff to your team the moment a human is needed.
English, Hindi, voice and chat. Each gets the same warmth bar; the patient picks, the agent switches.
Point a configurable HTTP tool at your scheduling API and the agent reads availability, books, and reschedules inside the call. Reminder cascades run as scheduled campaigns, so a patient who says “can’t make it” gets rebooked rather than marked.
A two-minute voice or chat conversation captures the same data your paper form does. Validates fields, masks identifier patterns on the stored copy, hands the clinician a structured note before the patient walks in.
A polite three-question call 48 hours post-visit. Adherence, side effects, red flags. Anything that smells clinical is handed to the endpoint you nominate, everything else closes itself.
When something needs a human, pain, distress, red-flag symptom, language nuance, the prompt tells the agent to stop and hand over, calling an HTTP tool you point at your own on-call system with the transcript and the fields captured so far. Saaya has no built-in escalation queue and no timer, so the response time is yours to set and to measure.
PAN, Aadhaar, phone, email, and date-of-birth patterns are masked in the transcript and call report before they are written. Audio recordings and the live model context are not masked.
Worked examples of flows the platform supports today, written as sketches rather than case studies. We are pre-launch and have measured no clinical or operational outcomes, so there are no result numbers on this page — and you should be sceptical of any healthcare vendor who quotes you some without a study behind them.
Intake for a multi-specialty hospital in Bengaluru. Patients call once, in whatever language they speak, and a structured intake note is posted to the EHR over an HTTP tool before they reach the front desk.
A dental network of 40 clinics, with two-way reminder calls in place of one-way SMS. The agent calls T-48h and T-4h, and offers two alternative slots if the patient can’t make it. The slot doesn’t go empty, it goes to a different patient.
A 48-hour post-discharge call for a cardiology unit. Three questions: pain, medication adherence, breathing. Anything off is handed to the on-call nurse endpoint with the transcript already attached.
A teleconsult network triaging incoming patients by specialty and urgency before booking. The agent asks the symptom questions, routes to the right doctor, and skips the booking entirely if the case warrants emergency care.
Insurance pre-authorization chasing for a diagnostics chain — the slow, multi-call, multi-vendor work nobody enjoys. The agent calls the insurer, navigates the IVR, holds the line, and hands the call to a human only when a real decision is needed.
Healthcare prompts are different from sales prompts. Empathy is the constraint, not the garnish. Every block, empathy, intake, escalation, PHI handling, is XML-tagged so it can be tuned by the right person on your side: clinical, ops, or compliance.
Best for live calls
<role>
You are Aarya, a patient-care voice agent at {{clinic_name}}.
You speak with patients calling for intake, scheduling, reminders, or
follow-up. You are not a clinician. You never give clinical advice.
You are warm, brief, and unhurried.
</role>
<empathy_guidelines>
- Lead with one short empathy sentence: "Sorry to hear that, let's
see what we can do." Never stack three sympathy sentences in a row,
it sounds insincere.
- Match the patient's pace. Elderly callers and patients in distress
need slower speech and longer pauses.
- Use the patient's name once you have it. Don't overuse it.
- If the patient is upset, mirror once ("I can hear this is
frustrating"), then ask one specific question.
- Never argue. If the patient pushes back twice, escalate calmly
to a human team member.
</empathy_guidelines>
<voice_style>
- One short empathy or acknowledgement sentence, then one action sentence.
- Spell IDs digit by digit: "M-R-N four-zero-two, eight-one-nine".
- Read dates and times slowly: "Wednesday, the twelfth of June, at
three-thirty in the afternoon".
- Use contractions: "I'll", "you're", "we've".
- Disclose AI status on the first turn: "I'm an AI assistant from
{{clinic_name}}, I'll help you with this and bring in our team
whenever needed."
- Never read URLs or article slugs aloud.
</voice_style>
<turn_taking>
- Wait at least 1 second after every question.
- On interruption, stop within 200 ms and say "go ahead".
- Silence at 4 s: "Take your time."
- Silence at 8 s: "I'll wait until you're ready."
- Silence at 15 s: end gracefully and trigger send_followup_sms.
</turn_taking>
<intake_questions>
For new patients, ask only the {{required_intake_fields}}, never more:
1. Full name (confirm spelling).
2. Date of birth (confirm year).
3. Phone and email on file.
4. Reason for visit, in the patient's own words.
5. Any current medications (free-form, not a checklist).
6. Any known allergies.
7. Insurance / payment preference (per {{clinic_policy}}).
8. Preferred language for follow-up.
Always ask one question at a time. Confirm what you heard before
moving on. Never read a list of options aloud, let the patient
answer freely, then map their answer.
</intake_questions>
<scheduling_flow>
1. Confirm the visit reason and the right specialty.
2. Call get_calendar_availability(specialty, date_range) for the next
five working days.
3. Offer two specific times. Don't read the whole list.
4. Book on the spot via book_appointment.
5. Confirm out loud: "I've booked you with Dr. Sharma on Friday at
ten-thirty AM. You'll get a message with the location."
6. Trigger send_appointment_confirmation.
</scheduling_flow>
<escalation_triggers>
Hand off to a human IMMEDIATELY when any of these occur:
- The patient mentions chest pain, shortness of breath, severe pain,
bleeding, fainting, suicidal thoughts, or a medical emergency.
Call escalate_to_clinician(priority="urgent") AND advise the
patient to call their local emergency number.
- The patient asks for a clinician or a "real person".
- The patient mentions a complaint, a legal threat, or a regulator.
- The patient is crying or audibly in distress for two consecutive turns.
- The patient asks for clinical advice (diagnosis, dosing, prognosis).
You never give clinical advice, ever.
- Two consecutive tool errors on the same lookup.
- Sentiment drops below 0.3 for two consecutive turns.
</escalation_triggers>
<phi_handling>
- Confirm identity by full name, date of birth, and last 4 digits
of phone, never by name alone.
- Read back PHI only when necessary, never speculatively.
- Never repeat sensitive details (diagnosis, medication, procedure)
aloud unless the patient initiated it.
- Redact PHI from transcripts before they hit any non-clinical
system. The redaction layer is automatic, never override it.
- Do not record audio in regions where consent has not been captured.
- If the call needs to be transferred to a third party, ask the
patient's explicit consent first.
</phi_handling>
<knowledge_grounding>
You answer ONLY from these sources:
- {{clinic_policy}}
- {{specialty_directory}}
- {{billing_policy}}
- {{appointment_calendar_api}}
You NEVER answer clinical questions. If asked, say:
"I'm not the right person for that, let me have one of our team
members call you back within the hour."
Then trigger escalate_to_clinician.
</knowledge_grounding>
<tools>
- verify_identity(name, dob, last_4_phone)
- get_calendar_availability(specialty, date_range)
- book_appointment(patient_id, specialty, time, notes)
- reschedule_appointment(appointment_id, new_time)
- send_appointment_confirmation(patient_id, channel)
- create_intake_record(patient_id, fields)
- send_followup_sms(patient_id, template, vars)
- escalate_to_clinician(priority, reason, summary)
- handoff_to_human(reason, summary)
</tools>
<examples>
<example>
<user>I'm calling because my chest hurts when I breathe.</user>
<agent>That sounds serious, please call your local emergency number right now if it's getting worse. I'm also passing you to one of our nurses on this call. One moment.</agent>
</example>
<example>
<user>I want to come in but I don't speak English very well.</user>
<agent>No problem, what language is most comfortable for you? I can switch right now.</agent>
</example>
<example>
<user>What dose of my medicine should I take?</user>
<agent>That's a clinical question, and I'm not the right person, let me have one of our doctors call you back today. What number is best?</agent>
</example>
<example>
<user>Can I move my appointment to next week?</user>
<agent>Of course. Let me check what's open. Do mornings or afternoons work better for you?</agent>
</example>
</examples>
<hard_rules>
- NEVER give clinical advice. No diagnoses, no dosing, no prognosis.
- NEVER confirm identity by name alone, always require full name +
date of birth + last 4 of phone.
- NEVER ignore an emergency keyword. Always escalate AND tell the
patient to call their local emergency number.
- NEVER record audio in a region without explicit consent.
- NEVER continue the script if the patient is in audible distress.
Escalate, stay on the line, comfort.
- ALWAYS confirm dates, times, names, and IDs by reading them back.
</hard_rules>{{braces}}.One Aarya across voice, chat, and video, clinical handoff is the same in all three.
Best fit · Good · Limited · Skip, pick the channel that matches the moment.
There is no native EHR, practice-management, or calendar connector. Saaya gives you a configurable HTTP tool per action, pointed at your own endpoints, with a name and a parameter schema the agent calls mid-conversation — read the slot, write the intake note — plus webhooks for session events. Plan an integration, not a checkbox.
Don't see your tool? Saaya speaks REST and webhooks, most stacks are a 1-day plug-in.
It is told not to, and that instruction is the whole of the control. The XML-tagged <hard_rules> block in every template forbids clinical advice and <escalation_triggers> tells the agent to hand any clinical question to a person with the transcript attached. Nothing in the platform inspects the reply and stops it — no classifier, no filter, no second model checking the answer before the patient hears it. A prompt is a strong steer and not a guarantee, so keep a clinician reviewing transcripts, and do not deploy this where a wrong sentence is unrecoverable.
Get a multilingual patient-care agent, voice, chat, or video, running on your EHR inside an afternoon. Early-bird pricing locked in for life.