Aakhyan: An AI-Powered Vernacular Patient Communication Platform for Oncology in Resource-Limited Settings - System Architecture and Pilot Randomised Trial Protocol

Q: Is the system hallucination-free?

Aakhyān does not use LLM-generated text in patient-facing output. Discharge instructions are rendered through deterministic, clinician-reviewed templates that have been approved by oncologists and clinicians. The AI extracts structured data from discharge summaries — the template converts that data into natural-language instructions. No generative text reaches the patient.

Q: Which languages does Aakhyān currently support?

Aakhyān currently generates discharge instructions in Bengali, Hindi, English, and Assamese, all delivered as WhatsApp voice messages. The system is designed to expand to additional Indian languages as the platform scales beyond the pilot.

Q: What happens when a drug isn’t in the lexicon?

If a medication is not yet in Aakhyān’s verified drug lexicon, the system flags it for clinician review. The drug’s brand name is preserved as-is, and every message is reviewed by the treating clinician before delivery. The clinician can edit, correct, or reject any instruction before it reaches the patient. No message is ever sent without clinician approval.

Q: What’s the cost per patient?

During the pilot at Silchar Cancer Centre, Aakhyān is provided at no cost to the institution or the patient. At scale, the system costs INR 100–150 per patient (approximately USD 1.20–1.80) — comparable to the cost of one blood test. This covers the full communication sequence: discharge comprehension, medication adherence nudges, follow-up reminders, and on-demand information access. The project is principal-investigator initiated and self-funded.

Q: How is patient data protected under DPDPA?

Discharge summary photographs are captured in-app and not saved to the device camera roll. Patient identifiers — names, addresses, contact details, and hospital IDs — are algorithmically redacted before any external API call. Only de-identified clinical content is processed using secure, access-controlled AI services. No patient names, contact details, or identifying information are ever exposed to external systems. Voice messages are delivered directly to the patient’s registered WhatsApp number. All processing complies with the Digital Personal Data Protection Act, 2023.

Q: Can another hospital join the pilot?

The current pilot is at Silchar Cancer Centre. Onboarding a new centre is straightforward — no integration with existing hospital systems is required for basic deployment. If you represent a hospital or cancer centre, please reach out through our contact form.

Q: Does Aakhyān support only cancer patients?

Currently, yes. Aakhyān is designed and validated for oncology discharge summaries, where the complexity of multi-drug regimens — chemotherapy, immunotherapy, targeted therapy, and hormonal therapy — and the consequences of misunderstanding are highest. We have long-term plans to extend the platform to other specialities where discharge communication gaps affect patient outcomes.

Q: Does Aakhyān only send messages at discharge?

No. Aakhyān continues through the post-discharge recovery window — 30 days in the current pilot protocol. Day 0 is six structured voice messages explaining the discharge summary. Days 1–3 bring risk-stratified medication adherence nudges. An on-demand keyword reply menu is available any time — the patient texts a number to hear their medicines, hospital contact, follow-up details, or diet advice. Two days before follow-up, verified reminders arrive with department, tests, and fasting instructions. A day-before availability check (currently in pilot phase) verifies doctor and equipment availability to prevent wasted journeys.

Q: Does the patient need to install an app?

No. Everything works through WhatsApp, which the vast majority of Indian smartphone users already have. Voice messages, medication nudges, follow-up reminders, and the on-demand information menu all arrive as standard WhatsApp messages. The reply commands (1 for medicines, 2 for hospital, etc.) use simple text replies — no app, no login, no learning curve.

Purkayastha, Debaraj Shome

doi:10.64898/2026.04.15.26350965

The Research

Aakhyān is being evaluated through a pilot randomized controlled trial at Silchar Cancer Centre, Assam.

Study Progress

Protocol

Study protocol finalised. Prospective, assessor-blinded, parallel-group pilot RCT with stratified block randomisation.

Protocol Preprint

Protocol paper published on medRxiv (April 2026). doi:10.64898/2026.04.15.26350965

Ethics (IEC)

Institutional Ethics Committee approval — application submitted.

CTRI Registration

Clinical Trials Registry of India — follows IEC approval.

Enrolment

100 patients (50 intervention, 50 control). Estimated 10–14 week enrolment period.

Results

30-day follow-up per patient. Preprints will be published on this page.

Publications

Preprint

Aakhyan: An AI-Powered Vernacular Patient Communication Platform for Oncology in Resource-Limited Settings — System Architecture and Pilot Randomised Trial Protocol

Purkayastha, D. S. (2026). medRxiv. doi:10.64898/2026.04.15.26350965

Abstract on medRxiv →Full PDF →

Study Design

Prospective, randomised, controlled, single-centre, open-label preliminary efficacy and feasibility study with stratified block randomisation by discharging consultant. 100 oncology patients (50 intervention, 50 standard care) at Silchar Cancer Centre, Assam. Reported per CONSORT extension for pilot and feasibility trials and WHO mERA checklist.

Intervention Group

Standard discharge + Aakhyān WhatsApp voice messages (6-message discharge sequence, medication nudges, follow-up reminders). Structured teach-back at 48–72 hours.

Control Group

Standard discharge only (printed summary + verbal explanation). Same teach-back assessment at 48–72 hours for comparison.

Primary Outcome

Discharge instruction comprehension at 48–72 hours post-discharge, measured by structured teach-back assessment.

Five domains, each scored 0–2 (total 0–10):

Medications — can the patient name their medications and what each one is for?
Frequency/Timing — does the patient know how many times a day and when to take each medicine?
Food relation — does the patient know which medicines to take before or after food?
Red flags — can the patient name warning signs that should bring them back to hospital?
Follow-up — does the patient know when their next appointment is and what to bring?

Adequate comprehension: score of 7 or higher, with a safety-floor rule — a score of 0 on dosing schedule or warning signs domains counts as inadequate regardless of total.

Secondary Outcomes

System Feasibility

Pipeline completion rate (target: ≥90%)
Median approval time (target: ≤30 minutes)
Message delivery rate (target: ≥95%)
Patient engagement (target: ≥40%)
Patient opt-out rate (target: ≤10%)

Clinical (Exploratory)

Follow-up attendance and timeliness
Pre-investigation completion
30-day emergency department visits
Self-reported medication adherence
Patient and caregiver satisfaction

Protocol Details

Study Title: Aakhyan: Preliminary Clinical Efficacy and Feasibility of an AI-Powered Vernacular Patient Communication System for Oncology Patients
Reporting Standards: CONSORT extension for pilot and feasibility trials · WHO mERA checklist · DPDPA 2023 compliance documentation
Follow-up: 30 days per patient
Status: Protocol finalised. IEC application submitted. Enrolment commencing 2026.

This is a pilot trial — preliminary evidence from 100 patients at a single centre, designed to inform a future multi-centre study. Results will be published on this page as the pilot progresses.

For the post-discharge communication architecture being evaluated in this pilot, see The Platform. For the clinician workflow, see For Clinicians. For regulatory alignment, see NABH Standards.