Abhijeet Vaidya, Head of Data and AI Practice, Emids, on how emids is structuring the next phase of data, context, and care delivery.
This observation frames much of the perspective shared by Abhijeet Vaidya, Head of Data and AI Practice at Emids, during a detailed discussion on the future of healthcare technology. As the industry experiences one of its most transformative phases in decades, Vaidya outlines how AI’s role is expanding from enhancing data utilisation to reshaping workflows and influencing organisational strategy.
“Technology does not replace people; it changes what people are able to do.” ~ Abhijeet Vaidya, Head of Data and AI Practice, Emids
This story distils the central themes and insights from that conversation, focusing on industry-relevant developments, enterprise challenges, and Emids’ evolving approach to AI.
Evolving with the Industry: Emids’ Shift Toward an AI-First Model
For nearly three decades, Emids has operated across the healthcare value chain. Its journey mirrors the wider evolution of healthcare IT from platform implementation to digital engineering and, now, to AI-driven solutions.
Today, the company positions itself as an AI-first organization, building a healthcare-specific agentic platform called PACCA AI. The system is designed to offer reusable components for orchestration, documentation, responsible AI, and domain-specific workflows, laying the groundwork for Emids’ broader service-as-a-software strategy.
Human Roles and AI: From Replacement to Reallocation
Vaidya emphasizes that the framing of AI “replacing humans” misrepresents its current and intended use. Instead, he describes AI as a tool that reallocates human effort more efficiently.
Examples include:
- Rural India: Local practitioners can now digitally share diagnostic records with specialists in major cities, supported by AI systems that extract and structure medical data.
- Clinical visits: Ambient AI tools automatically capture medical notes, reducing administrative load on physicians.
- Home health services: Automating referral and fax-based processes allows clinicians to focus on patients rather than paperwork.
In all instances, AI shifts work toward higher-value clinical or analytical tasks.
Digital Adoption and Access: A Gradual but Measurable Change
Although digital literacy continues to vary across regions, Vaidya notes growing comfort with digital interactions in both India and the U.S. Telemedicine, which existed for years, saw widespread adoption only after COVID-19 normalised remote consultations.
As India builds out digital public infrastructure and health records, AI is expected to support broader access to healthcare, particularly in tier-two and tier-three regions. According to Vaidya, the shift is gradual but progressing in a consistent direction.
AI in Healthcare: Practical Gains Over Generic Use Cases
Rather than focusing on high-level AI examples, Vaidya highlights two foundational areas shaping real-world impact:
1. The Longitudinal Medical Record
Healthcare’s long-standing challenge has been the lack of unified, lifelong patient records. AI now enables:
- Extraction of insights from unstructured data (which comprises up to 90% of healthcare data)
- Interoperability across diverse systems
- More informed clinical decision-making
2. Reducing System Friction
AI can streamline administrative workflows, such as:
- Prior authorization processing
- Claims classification
- Routing of medical information
- Referral and intake procedures
These improvements reduce delays, support continuous workflows, and improve operational efficiency.
Why ROI Often Falls Short: Context Matters
Responding to concerns about low ROI in AI projects, Vaidya explains that the root causes often stem from enterprise readiness rather than the technology itself.
Key challenges include:
- Legacy systems unable to accommodate AI outputs
- Insufficient change management, leaving teams unprepared for new workflows
- Lack of integration with business processes, limiting the practical application of AI solutions
He cites an example where an AI solution was built in two months, but the receiving system required three additional months of modification to support automated outputs. The lesson: AI success depends heavily on enterprise architecture and operational context.
The Startup Landscape: Competition and Consolidation
Vaidya takes a neutral view of the rapid proliferation of AI startups and ongoing discussion about an “AI bubble.” While many new companies may not survive long-term, he sees competitive entry as a characteristic of a healthy market.
Startups, he notes, must focus not only on technological innovation but also on:
- How their solutions integrate into existing healthcare systems
- Enterprise-grade requirements such as compliance, interoperability, and security
Without this, even strong products may struggle with real-world adoption.
Five Pillars of Emids’ Long-Term Vision
Emids’ strategy for becoming a service-as-a-software organization is built on five components:
- PACCA AI Platform – Core enabler for agentic healthcare solutions
- Domain Ontologies – Micro-domain knowledge structures for claims, care coordination, utilization management, and member services
- Forward Deployed Context Engineers – Professionals trained in healthcare, enterprise architecture, and AI
- Ecosystem Partnerships – Collaboration with hyperscalers, niche firms, and startups
- Outcome-Based Pricing – Aligning cost models with measurable client outcomes rather than resource-based billing
This framework aims to integrate AI meaningfully into healthcare operations rather than treating it as a standalone product.
Guidance for Emerging Professionals
For students and early-career professionals entering AI, Vaidya recommends:
- Building strong fundamentals beyond prompt engineering
- Understanding enterprise architecture and system interactions
- Learning regulatory and responsible AI frameworks, especially in healthcare
- Recognizing how solutions operate in practical, large-scale environments
These skills, he notes, are critical for sustainable careers in the sector.
Looking Ahead
As India and global healthcare systems expand their digital foundations, the role of AI is expected to grow correspondingly. Vaidya sees AI as an enabler that can help improve access, efficiency, and system capacity.
The central takeaway from the conversation is clear: AI’s effectiveness depends not only on technological capability but also on how organizations integrate it into existing structures, train their workforce, and design sustainable, context-aware solutions.