Puneet Maheshwari is the senior vice president and general manager for Optum Real at Optum.
On any given weekday morning, care providers nationwide sit staring at computer screens, navigating and cross-checking patient approval and reimbursement forms. On average, the paperwork to schedule and process an MRI takes up to seven business days to complete. The scan itself takes only about 45 minutes. It’s an increasingly common scenario: Providers spend more time on administrative processes than on patient care.
Artificial intelligence is being deployed across claims processing at remarkable speed, promising faster decision-making, reduced errors and billions in savings. But the real opportunity isn’t incremental efficiency. It’s reimagining the end-to-end claims and reimbursement experience for providers, payers and patients.
In 2026, here are five ways we can leverage AI with human in the loop to redesign the health care claims system:
1. From fragmented tools to seamless systems
Health systems have made progress by adopting AI-powered point solutions for tasks like patient information management, eligibility checks and claims processing. However, these tools often operate in isolation. Proprietary APIs and siloed software create fragmented workflows, data inconsistencies and compliance risks, turning initial efficiency gains into new complexities.
The solution is integration and interoperability. Organizations should move toward unified platforms built on standardized APIs and cloud-based infrastructure, enabling seamless data exchange across payer and provider systems. This approach eliminates duplicate tests and minimizes manual reconciliation. Governance frameworks and phased implementation will ensure compliance and scalability, while embedding AI-driven analytics provides oversight and unlocks the full potential of connected systems.
2. Reduce claims processing burden
With better data flow, we can finally address the paperwork that slows down approvals. About 85% of first time claims denials are avoidable, often due to incomplete or inconsistent data. AI can help, but even high-performing providers face the same cumbersome review process as everyone else.
Why should a cardiology practice with a 95% approval rate for stress tests endure the same paperwork as a new clinic? As the healthcare landscape continues to shift, organizations should pioneer dynamic auto-approval pathways that go beyond traditional gold carding. Qualified practices meeting eligibility thresholds could bypass standard prior authorization entirely.
The payoff: fewer claims clogging the system, reduced administrative burden and more time for patient care.
3. Building actionable accountability
Streamlined approvals only work if decisions are clear and easy to act on. When claims are denied, patients and providers often receive vague explanations such as “criteria not met” with no specifics. This opacity fuels frustration, resubmissions and delays.
To stay ahead, organizations should implement explainable AI that provides detailed reasoning with every coverage decision. When a claim is denied, the system specifies exactly which criteria weren’t met and what documentation would support approval. This change would prove that accountability and efficiency aren’t competing values: Appeals decrease because providers understand requirements upfront, payers’ first-pass approval rates improve as documentation gets better and patient trust scores increase when decisions are transparent.
4. Industry collaboration to achieve real cost transparency
When everyone understands the decision, we need to make sure costs are clear for healthcare stakeholders. Yet, cost transparency has often fallen short of expectations. The issue isn’t AI’s ability to calculate costs. It’s access to complete and accurate data.
Pricing remains buried in proprietary contracts between payers and providers. Negotiated rates vary by patient and plan, and the information needed for accurate estimates sits fragmented across incompatible systems. This opacity leaves providers uncertain about reimbursement and payers struggling to predict actual costs.
In 2026, the industry can address this gap by enabling real-time access to pricing and benefit data across payer and provider systems. Integrated platforms that expose negotiated rates, accumulators and coverage details before care is delivered will help providers guide decisions confidently, and allow payers to forecast expenses accurately. This shift turns cost clarity into a practical standard rather than an elusive goal.
5. Enabling transparent care coordination
After achieving cost clarity, we need to give patients visibility into their care journey. Historically, claims systems operated behind the scenes, leaving patients unaware of coverage details until bills arrived. This lack of transparency creates confusion and delays.
AI can change that by providing real-time information on coverage, prior authorizations and out-of-pocket costs before services are delivered. Patients can see what care is approved, what it will cost and what steps come next, such as scheduling follow-ups or coordinating with specialists.
This shift from opaque processes to patient-centered coordination builds trust, reduces surprises and ensures patients are active participants in their care. It improves satisfaction and outcomes while helping organizations manage costs effectively.
The path forward in 2026
Claims processing has frustrated every stakeholder for decades. These six strategies represent more than faster turnaround times. They reimagine the system around patient interests and operational efficiency. Organizations embracing this shift are already seeing measurable results including lower administrative overhead, stronger provider relationships and improved patient satisfaction.
The convergence of technology, regulation and market dynamics creates a rare opportunity for transformation. It is time for healthcare to rise to the occasion.
