Agent AI and healthcare transformation architecture

In 2010, the Affordable Care Act redefined access to health care. Fifteen years later, we find ourselves facing another challenge – not who can get into the system, but how the system works once it enters.

We now spend more than $5.1 trillion on health care each year, accounting for nearly 19% of GDP, up from 17.9% in 2010, although the population increased by only 10% over the same period. Coverage has increased from 84% to 92%, but costs are increasing and operational complexity has reached a breakthrough point. Even with better coverage, public trust in the system is declining: According to recent polls, only 36% of Americans believe that the quality of the U.S. health system is in line with the cost paid, while 54% of the quality is unfavorable.

We have achieved wider access. However, we have not optimized the system yet.

The burden under the surface

While clinicians and patients experience obvious care frictions (otherwise, denial, documented), the potential burden is operational. Manual processes can still drive some of the most critical interactions in healthcare.

• The prior authorization process for each request may take more than 35 minutes. Large-scale health plans may process 80,000-100,000 authorizations per day.
• Medical record reviews, appeals and complaints, and claims processing require human interpretation of unstructured data – law, handwritten notes, PDF.
• Nursing gap reviews and risk and quality assessments often lag behind real-time decision-making, thus reducing the effectiveness of value-based care models.

This is not limited to the payer. Similar challenges have overwhelmed hospitals and health systems.

• The team must manually abstract the data for clinical research or quality reporting.
• Verification requests for transactions must be initiated and tracked, with little visibility to the payee's status.
• Multidisciplinary nursing teams are coordinated by email, EHR notes or spreadsheets – often leading to duplication, missed opportunities and clinician burnout.

All of this consumes a lot of resources: More than 25% of U.S. health care spending is attributed to administrative costs. At the same time, patient experience stagnates, employee burnout increases and trust prevalence.

Built for incrementalism rather than transformation

One of the reasons we didn't solve it is that the system was built for slow changes. ACA assumes a stable regulatory environment and gradual improvement, besides all advantages. It embeds programs such as the patient-centered outcomes institute and national quality strategies, but fails to foresee the scale, speed and complexity of health care over the subsequent decade.

It did not foresee the rise of vertically integrated healthy conglomerates, with private equity confidence or market capability concentrated in fewer participants. It also doesn't expect how technology will evolve (AI, machine learning, self-service diagnosis and big data), and healthcare systems are still largely under the surface.

As a result, ACA focuses on coverage. The bet is that as more people in the system, operational improvements will be made subsequently. But that bet was not eliminated. The administrative process is largely untouched and has stifled innovation by regulatory prudence and stakeholder boycotts.

Today, our systems seem digital, but still deeply manual the core.

Cause damage from the top?

Recent political shifts have increased more urgently. At his Senate confirmation hearing, Dr. Mehmet Oz, the upcoming CMS administrator, made it clear: Change is coming.

Oz constructs the current healthcare model, a shockingly entrenched interest by “150 people don’t want to change anything”. He calls for real-time data usage, smarter workflows, and tools that empower patients and clinicians. He highlighted the AI-driven transformation in areas such as previous authorizations, while also warning of its potential abuse and calls for oversight.

His position emphasizes the tension at the center: we need to change, but the construction of the system cannot change rapidly. That is where proxy AI is not only useful, but it is also essential.

What is proxy AI?

Proxy AI refers to intelligent, task-specific systems that can ingest complex data, interpret context, cause across standards and work with humans, all in real time. Unlike traditional automation or rule-based robots, these agents do not require detailed programming. They learn, adapt and integrate into existing workflows.

They are not tools. They are colleagues.

This is what they enable throughout the healthcare ecosystem.

For the health plan:

• Prior authorization: Guide rulings while at the same time as Carelon, equitable and internal standards, and develop instant, interpretable decisions.
• Nursing Gap Review: Active Alerts Based on Real-time Layered Member Data.
• Medical Record Review and Appeal: Summary and Index of Clinical Narratives from Free Text EMR, PDF and Images.
• Claim Handling: Mark inconsistencies and accelerate solutions through structured, reasonable decision support.
• Expense schedule and risk assessment: Adaptive matching of code, pricing model and risk adjustment metrics.

For use in hospitals and health systems:

• Clinical study: Accelerate cohort selection by unstructured annotation of eligible markers.
• Nursing Management: Comprehensive risk factors and interventions to guide high-point touch-hold exhibitions.
• Revenue Cycle Management: Documentation for automatic authorization, appeals and compliance reviews.
• Nursing Coordination: Dynamics, Dynamics, AI-powered decision-making summary for multidisciplinary teams, visibility into the latest clinical events, gaps and patient preferences.

All of these cases have a theme: Reducing the burden through intelligent collaboration.

The “way” of transformation

So, how does proxy AI achieve this?

1. Complexity of mass intake – these systems are designed to handle faxes, scan documents, handwritten notes, EMR outputs and claim files – on one hand. They don't need perfect data. They thrived in a chaotic ecosystem.
2. Understanding the Healthcare Environment – ​​Agent AI does not consider everything as universal content, but rather understands healthcare-specific terms, regulatory requirements, and clinical pathways. It not only reads the chart, but also knows what is related, what is missing and what it means.
3. Work with transparency – Every insight can be traced back to its data source. Every action is reviewed. Each suggestion can be explained. This is not Black-Box AI, it is the co-pilot intelligence built for a regulatory-grade environment.
4. It can adapt to policy shifts (as regulations develop), either under Trump or any future administration, to quickly update proxy AI systems to reflect new requirements, preventing expensive retraining and hard-code failures.
5. Scale of the Whole Business – Once a priori AUTH is embedded, the same infrastructure can be applied to medical reviews, appeals, risk analysis and even clinical studies, creating a transformational flywheel.

Why now?

Because we can't wait. The next government (whoever) will face enormous pressure to reduce costs, simplify delivery of care and restore public trust. This means a more stringent scrutiny of Medicare’s advantages, potential Medicaid reforms, and payer practices.

Health plans and providers must now prepare – not fear, but be prepared. Ready won't come from more manual hires or new portals. It will come from a system of thinking, adapting and supporting.

Proxy AI is not only a technical strategy. This is a resilient strategy.

From ability to ability

Healthcare successfully expands its capabilities – more participants, more technology, more data – but the raw capacity alone can be overwhelming rather than authorized. A key step forward is to transition from simple ability to real ability. We need smart, scalable systems to increase burden, illuminate connections, and empower healthcare professionals to deliver the highest quality of care.

Proxy AI provides this basic functionality. It shifts complexity from a source of burden to a source of opportunity, and healthcare from responsive compliance to proactive, insightful care. The real transformation doesn’t happen in the headlines – it unfolds in the daily workflow that healthcare provides.

We must stop demanding more effort from stressful systems and invest in systems designed to expand human potential. The future of healthcare is more than just digital – it is smart collaboration. The future is an agent.

Images: Yuichiro Chino, Getty Images

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