The Patient Safety Deficit
Clinical Agents Are Making Decisions. Patient Lives Are at Stake.
India has one of the worst doctor-patient ratios in the world. AI agents are stepping in to triage, diagnose, and recommend treatments — but with what oversight? The scale of the crisis is forcing adoption faster than governance can keep up. When an agent recommends a drug interaction or triages a chest pain patient, there's one doctor for every 1,500 Indians who might catch an error. These aren't chatbots — they're clinical decision-makers operating with near-zero human review.
Our Approach
Clinical Agents Need Governance, Not Just Accuracy Scores
Most health-AI vendors ship a model validated on a curated dataset and call it done. Agentic clinical systems — where AI reasons across symptoms, lab results, and imaging to recommend treatment — need a fundamentally different safety architecture.
Clinical Agents Without Operations
- Deploy diagnostic agent, validate on curated test dataset
- No central registry of clinical agents or their risk levels
- No reasoning trace for clinical decisions — black box diagnosis
- Compliance reviewed only after a patient safety incident
- No monitoring for demographic or regional bias in outcomes
- Hope the agent doesn't hallucinate drug interactions or contraindications
Clinical Agents with Rotavision
- Every agent registered with clinical risk classification and autonomy boundaries
- Reasoning capture for every diagnostic recommendation — full audit trail
- Human-in-the-loop mandatory for treatment decisions — bounded autonomy by design
- Continuous monitoring for demographic and regional bias in clinical outcomes
- Hallucination detection for drug interactions, contraindications, and dosage errors
- ABDM-compliant health data exchange with consent management and complete audit trails
Where It Matters
Agentic AI for India's Healthcare Reality
Not generic clinical models — autonomous agents solving the specific problems India's overstretched healthcare system faces every day.
India's rural primary health centres (PHCs) serve populations of 20,000–30,000 people, often staffed by a single medical officer with no specialist backup. With a doctor-patient ratio of 1:1,500 nationally — and far worse in rural districts — AI agents are being deployed to assist with differential diagnosis, treatment protocol selection, and drug interaction checks. These agents don't just answer questions; they reason across symptom histories, lab results, and local disease prevalence to produce recommendations that doctors act on.
But clinical AI must suggest, not decide. When a triage agent at a PHC in Chhattisgarh routes a patient with chest pain to a district hospital 80 kilometres away, the reasoning must be traceable — in the clinician's language, not in developer logs. When an agent recommends a treatment protocol, the evidence citations must be verifiable, the confidence level transparent, and the escalation path clear. Bounded autonomy means the agent operates within clinical guardrails and escalates when it encounters cases outside its training distribution.
Vishwas ensures every clinical agent recommendation is explainable — with reasoning traces in 22 Indian languages, bias monitoring across demographic categories, and evidence citations linked to clinical guidelines. Orchestrate enforces bounded autonomy, manages agent registration with clinical risk classification, and ensures human-in-the-loop approval for all treatment decisions.
India runs some of the world's largest screening programmes — for tuberculosis, diabetic retinopathy, cervical cancer, and more. The volume of imaging far exceeds available radiologist capacity. AI agents are being deployed to read chest X-rays in TB screening camps, analyse retinal images for diabetic retinopathy in district hospitals, and flag suspicious findings in CT scans at tertiary centres. These agents process thousands of images daily, often as the first — and sometimes only — reader.
Scale without governance is reckless. A diagnostic imaging agent must flag its confidence level on every read. When confidence falls below clinical thresholds, the case must be escalated to a human radiologist automatically — not queued in a backlog. The agent must be monitored for drift as imaging equipment, patient populations, and disease prevalence change over time. A model trained on urban hospital CT scanners will behave differently on portable X-ray units in rural screening camps.
Guardian continuously monitors diagnostic imaging agents for accuracy drift, confidence degradation, and performance variance across equipment types and patient demographics. Orchestrate manages escalation pathways, enforces mandatory human review for uncertain cases, and maintains complete audit trails for every imaging agent decision — from initial read to final diagnosis.
India's healthcare challenge isn't just diagnosis — it's follow-up. Chronic disease management for diabetes, hypertension, and tuberculosis requires sustained patient engagement: medication adherence reminders, symptom monitoring, diet guidance, and appointment follow-ups. With 22 scheduled languages and hundreds of dialects, and significant portions of the population with low health literacy, this engagement must happen in the patient's own language and at their comprehension level.
Patient engagement agents are being deployed as vernacular health assistants — WhatsApp bots, voice-based IVR systems, and ASHA worker support tools that interact with patients in Hindi, Tamil, Bengali, Odia, and beyond. These agents handle symptom checking, medication reminders, and health education. But a medication adherence agent that misunderstands a symptom report or provides incorrect dosage guidance in a language it wasn't properly trained on is a patient safety risk, not a convenience feature.
Dastavez processes health records, prescriptions, and consent forms across India's multilingual document ecosystem — linking patient data to ABHA IDs with full consent management. Vishwas monitors every patient-facing agent interaction for accuracy, safety, and linguistic fidelity — ensuring health information is correct, contextually appropriate, and delivered at the right literacy level.
Solution Package
Clinical Agent Safety Accelerator
A combined assessment, platform, and integration package for hospitals and health systems deploying AI agents in clinical workflows — with CDSCO SaMD readiness and ABDM governance built in.
What's Included
Audit all clinical agents against CDSCO SaMD risk tiers and ABDM data access levels. Map each agent's clinical autonomy boundaries with a readiness roadmap.
Orchestrate + AgentOps configured for healthcare — agents classified by clinical risk (triage advisory vs diagnostic vs treatment), with escalation policies calibrated to risk level.
Agent outputs grounded against Indian Standard Treatment Guidelines (STGs), National List of Essential Medicines (NLEM), and WHO protocols. Flag when recommendations deviate from approved guidelines.
Pre-built connectors for ABDM Health Data Exchange, ABHA ID consent flows, and major HIS/EMR systems. Agent governance layer alongside clinical workflows.
Continuous monitoring of clinical agent performance — outcome tracking, adverse event detection, and demographic disparity analysis. CDSCO-ready vigilance reporting.
Patient Safety
Platform Stack
A billion Indians need healthcare they can access.
The question isn't whether agents will deliver it — it's whether anyone is governing the agents.
Platform
Recommended Products
Vishwas
Explainability & Fairness for Clinical Agent Decisions
Guardian
Agent Reliability & Drift Monitoring for Diagnostics
Orchestrate
Multi-Agent Orchestration with Human-in-the-Loop Controls
Dastavez
Document AI Agents for Health Records & Consent
AgentOps
Enterprise Agent Registry, Clinical Risk Classification & Audit
