Evaluating healthcare AI vendors in India requires a structured 8-domain framework that moves beyond polished demos to test real-world HIS/EMR integration, local clinical validation, and regulatory compliance. Hospital IT and clinical leadership must audit vendors across FHIR R4/ABDM interoperability, DPDP Rules 2025 data governance (including local data hosting and 72-hour breach reporting), CDSCO medical device standing, CERT-In security controls, MLOps model drift monitoring, and total cost of ownership. Applying a weighted scorecard—with strict minimum thresholds across clinical, technical, and commercial domains—prevents costly integration failures, secures legacy stack compatibility, and ensures safe clinical adoption.
Most healthcare AI projects in India do not fail because the model is inaccurate. They fail because the vendor was chosen on a demo, and the demo never had to survive your HIS, your network, your consent workflow, or your clinicians. Bain & Company's review of AI in Indian healthcare delivery makes the same point: the infrastructure is becoming AI-ready, but implementation readiness remains uneven, with data quality, workflow integration, clinician trust, and healthcare domain expertise alongside AI capability deciding who actually succeeds.
The stakes are rising with the market. India's AI in healthcare market is forecast by Ken Research to grow from roughly USD 1.3 billion in 2025 to USD 10.8 billion by 2031, a compound annual growth rate of about 41.7%. More money means more vendors, and more vendors means a harder selection problem for the hospital IT leader who has to defend the decision to the CMO, the CFO, and the compliance team at the same time.
This checklist is built for that person. It covers what technical teams must verify and what business leaders must negotiate, in the order that tends to expose weak vendors fastest.
Why Vendor Evaluation Goes Wrong in Indian Hospitals
Three patterns repeat across hospital AI evaluations.
The demo is run on clean data: Vendors present on curated sample records. Your production environment has scanned discharge summaries, free-text notes in mixed English and regional languages, duplicate patient IDs, and a HIS that may not expose a modern API. A vendor who has never integrated with your category of system will discover this after the contract is signed.
Integration is treated as a line item, not the project: The hospital's own legacy stack is often the hardest part of the build. A review of ABDM operations notes that many facilities run proprietary software with no ABDM-compliant API layer, and that integration costs for mid-sized providers are estimated at INR 30 to 50 lakh. If the vendor's proposal does not name your HIS and explain the integration path, the real cost has not been priced yet.
Governance is deferred: Consent, audit logging, and breach response are scoped as "phase two," which in practice means retrofitting them into a system already in production, the most expensive way to build them.
The Eight-Domain Evaluation Checklist
Score every shortlisted vendor against the same eight domains. Each domain below lists the questions your technical team should ask and the questions your business owners should ask, because a vendor can pass one conversation and fail the other.
1. Clinical Validation and Regulatory Standing
Technical & ROI questions
Has the model been validated on Indian patient data, and on which sites? Performance published on Western datasets does not transfer automatically to Indian disease presentation, imaging equipment, or documentation style.
Was validation prospective (on live data going forward) or only retrospective (on historical data the model could have been fit to)?
What are the reported sensitivity, specificity, and false-positive rates, and for which sub-populations?
Does the product fall under the Medical Devices Rules, 2017 as software as a medical device, and does the vendor hold the relevant CDSCO status where required?
Does the vendor follow the ICMR ethical guidelines for AI in biomedical research and healthcare in how it handles data and evaluation?
Who carries clinical liability if the tool contributes to an error, and is that written into the contract?
2. Integration Architecture
Technical & ROI questions
Which standards does the system speak: HL7 FHIR R4, HL7 v2, DICOM, IHE profiles? ABDM mandates FHIR R4 for health record exchange, so a vendor with a proprietary export format is not ABDM-ready, whatever the brochure says.
Name three HIS or EMR products the vendor has integrated with in production, and describe the integration method for each (API, interface engine, database-level, file drop).
Is the integration read-only at first, and what controls exist before the system can write back into the clinical record?
Is integration work priced separately, and who pays third-party HIS vendor API or interface fees?
What happens to the integration if you change HIS vendor in three years?
3. Data Governance and DPDP Alignment
Technical & ROI questions
Where is data stored and processed, and does any patient data leave India, including calls to third-party model APIs?
How is consent captured, stored, and enforced at the data layer, and can a consent withdrawal propagate to the AI system?
Are logs tamper-evident, and how long are they retained?
Is there a signed data processing agreement that defines the vendor as a processor, with audit rights for you?
Can the vendor commit to supporting your breach response inside the 72-hour reporting window that the DPDP Rules, 2025 set for a detailed report to the Data Protection Board?
4. Security Posture
Technical & ROI questions
Encryption at rest and in transit, key management, role-based access control, and multi-factor authentication for administrative access.
Date and scope of the last independent penetration test, and remediation status of its findings.
Incident detection capability sufficient to meet both the DPDP breach clock and CERT-In's six-hour incident reporting direction.
Does the vendor hold ISO 27001 or SOC 2 Type II, and will they share the audit summary under NDA?
Is cyber insurance in place, and what does it cover?
5. Model Governance and MLOps
Technical & ROI questions
How are model versions tracked, and can you roll back within hours if performance degrades?
What drift monitoring exists, and what thresholds trigger retraining or a clinical review?
Is there a human-in-the-loop checkpoint for any output that affects care, coding, or billing decisions?
Is monitoring included in the contract, or sold later as a managed service?
Who owns the retrained model, and can you keep it if the relationship ends?
6. Implementation and Change Management
Technical & ROI questions
What does the first 30, 60, and 90 days look like, with named deliverables?
What environments will be provided (sandbox, staging, production), and who provisions network access?
Who specifically staffs the project, and what happens if a key person leaves?
How is clinician training delivered, and what adoption metric will be tracked after go-live?
7. Commercial Terms
Legal and compliance questions
Pricing model: per user, per study, per bed, per transaction, or fixed project fee. Ask for a three-year cost projection under each plausible volume scenario.
Exit terms: data export format, transition assistance, and deletion certification.
Service levels: uptime, support response times, and credits for missed targets.
Are there rate limits, API quotas, or usage caps that could throttle the system at peak load?
8. References and Track Record
Technical & ROI questions
Can you speak to a customer of similar bed count and HIS environment, and ask what slipped?
Did the timeline match the original proposal?
Can the vendor show a production system that has run for twelve months or longer, with a monitoring history?
A Weighted Scorecard You Can Use Tomorrow
Weighting forces the committee to agree on priorities before vendors present. Adjust to your context, but fix the weights before the first demo.
Domain | Suggested weight |
|---|---|
Clinical validation and regulatory standing | 20% |
Integration architecture | 20% |
Data governance and DPDP alignment | 15% |
Security posture | 15% |
Model governance and MLOps | 10% |
Implementation and change management | 10% |
Commercial terms | 5% |
References and track record | 5% |
Score each domain from 1 to 5, multiply by the weight, and require a minimum of 3 in every domain, so that a vendor cannot buy its way past a compliance weakness with an excellent demo.
Adapting the Checklist to Your Type of Hospital
The same eight domains apply everywhere, but the emphasis shifts with the size and structure of the organisation.
Single-specialty hospitals and standalone clinics usually have simpler systems and less IT staff. Weight implementation support and commercial terms higher, and favour vendors who can deliver a fully managed service. A clinic with one part-time IT person cannot absorb a vendor that expects the customer to run MLOps.
Mid-sized multi-specialty hospitals carry the heaviest integration burden relative to their IT capacity, because they often run a mix of HIS, LIS, and PACS from different vendors, sometimes installed years apart. Weight integration architecture at 25% or more, and insist on a named integration lead from the vendor.
Hospital chains and multi-site groups need configurability across sites, centralised monitoring, and consistent governance. Ask how the vendor handles site-level variation in documentation templates and workflows without forking the codebase, and whether one model governance process can cover all locations.
Government and public-sector facilities face procurement rules, data localisation expectations, and often lower connectivity. Ask about offline-tolerant design, procurement-friendly licensing, and alignment with national platforms. ABDM facility adoption data shows a sharp gap between public and private uptake, so check which environment the vendor has actually delivered into.
Whatever your profile, resist the temptation to evaluate AI vendors in isolation from your broader roadmap. If you are also planning an EMR upgrade or ABDM milestone work in the next 12 months, sequence the AI project so that it does not depend on an integration layer that is about to change.
Red Flags That Should Pause the Process
The vendor cannot name your HIS or EMR category and describe a past integration with it.
"Compliant" without specifics. Ask where data lives, who can see it, and how consent is enforced. Vague answers are the answer.
Patient data is sent to a general-purpose public AI API with no contractual control over retention or training use.
No post-go-live plan. If the proposal ends at deployment, drift and support are unpriced risks.
Accuracy claims with no population or dataset named.
A junior team behind a senior sales pitch. Insist on named delivery staff.
How to Run the Pilot So It Tells You the Truth
A pilot should be designed to expose weakness, not to confirm the sale.
Pick one bounded use case with a metric you already track: documentation minutes per consultation, claim rejection rate, report turnaround time, or missed appointment rate.
Use real data under a written agreement, not a sanitised extract, with de-identification where the use case allows.
Run the pilot inside your actual workflow, including the HIS integration, not in a standalone web tool.
Define success and failure thresholds in advance, including what happens at a no-go.
Measure adoption, not just accuracy. A tool clinicians route around has delivered nothing.
Review governance artefacts at the end: consent records, audit logs, and the incident response walkthrough.
Where Prognos Labs Fits
Prognos Labs is an AI consulting and development partner for hospitals, clinics, and health-tech teams in India. Healthcare is where the firm has shipped the most production systems, including ambient documentation and agentic claims and intake workflows that have cut documentation time by up to 65% and operational cost by 23% in client engagements. The delivery model is deliberately simple to evaluate against the checklist above: one accountable team across strategy, build, integration, and monitoring, with DPDP and HIPAA-aligned governance designed into the architecture from the first sprint rather than added later.
If you want to test any vendor, including us, against this scorecard, a free AI readiness consultation is a low-commitment way to do it.