Reimagining healthcare through AI-powered claims adjudication

 In brief

• AI-driven auto-adjudication of Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) healthcare claims reduces processing times from weeks to hours.
• A human-in-the-loop approach promotes trust, transparency and scalability. 

When scale meets complexity

The healthcare claims adjudication process involved the manual review of more than 40,000 claims daily by human reviewers, comprising hundreds of thousands of documents in varying formats, ranging from handwritten notes and scanned forms to complex medical imagery. These inconsistencies, combined with multilingual inputs and a lack of standardization, made manual processing slow, error-prone and difficult to scale.

Manual adjudication also brought inherent risks. Outcomes could vary depending on the evaluator’s experience, availability or interpretation, leading to delays, inconsistency and potential bias. As claim volumes continued to grow, so did the strain on resources, impacting hospitals through delayed payments and creating inefficiencies across the system.

Rethinking adjudication: From manual intervention to intelligent automation

Recognizing these challenges, the NHA adopted a technology-led approach centered on an Auto-Adjudication Engine (AAE). The objective was clear: to create a standardized, rule-based system capable of delivering consistent and transparent decisions at scale.

At its core, the AI-powered engine evaluates claims through a structured process, verifying mandatory documents, checking completeness and authenticity, confirming beneficiary consistency and validating clinical logic. By combining machine learning for healthcare claims with natural language processing, optical character recognition and large language models, the system enables AI for unstructured healthcare documents, processing complex data with speed and precision.

Importantly, the transformation was not approached as a plug-and-play solution. The NHA adopted a phased strategy, beginning with proof-of-concept implementations focused on high-volume claims. By aligning the system with Standard Treatment Guidelines issued by the Government of India and continuously refining models, the organization tailored the solution to real-world complexities rather than generic benchmarks.

Building trust: Why governance matters as much as technology

Technology alone does not drive transformation — trust does. In a public healthcare ecosystem, maintaining credibility with stakeholders is critical. To support responsible AI in healthcare, the NHA embedded strong governance practices throughout the implementation.

A human-in-the-loop approach helped AI complement, rather than replace, professional judgment. Continuous validation against traditional methods, coupled with real-time oversight and supported by auditable trails, helped build confidence in the system’s outcomes. Regular interactions with state-level stakeholders and transparent communication further reinforced adoption and alignment.

Data security and compliance were equally important. Given the sensitivity of healthcare data, robust safeguards and adherence to regulatory standards were essential to sustaining trust across the ecosystem.

Speed, efficiency and better outcomes

The shift to intelligent claims automation has delivered significant operational and business benefits. Processing timelines that once took weeks have been reduced to hours, enabling faster decision-making and accelerating payment cycles. Hospitals now experience quicker query resolution and improved clarity on documentation requirements.

The automation has also freed up human resources to focus on more complex, high-value cases, improving overall efficiency. At the same time, increased detection of non-compliance and greater consistency in decision-making have strengthened the integrity of the system.

Beyond adjudication: Enabling a smarter healthcare ecosystem

The impact of AI and healthcare process automation extends beyond claims processing. By integrating AI-driven tools such as chatbots and feedback systems, the NHA is enhancing beneficiary engagement and making services more accessible. From checking eligibility and locating hospitals to accessing treatment history and raising grievances, patients can now interact with the system more seamlessly.

This shift represents a broader move toward a digitally enabled healthcare ecosystem — one that prioritizes responsiveness, transparency and user empowerment.

Lessons for leaders: Scaling AI with purpose

For organizations navigating similar transformations, the NHA’s journey offers key insights. First, aligning AI initiatives with business context and operational realities is essential. Second, validation and iterative learning are critical to building confidence and maintaining accuracy. Finally, a balanced approach that combines technology with human oversight provides the foundation for sustainable and responsible AI adoption.

As healthcare systems worldwide grapple with rising demand and complexity, the NHA’s experience demonstrates how AI for large-scale public healthcare programs, when implemented thoughtfully, can unlock new levels of efficiency, consistency and trust, reshaping how large-scale operations serve millions.