In 2010, the Health Care Law at low price redefined access to medical care. Fifteen years later, we are facing a different type of challenge, not who can enter the system, but how the system works once they are inside.
Now we spend $ 5.1 annual billions in medical care, almost 19% of GDP, compared to 17.9% in 2010, despite only a 10% increase in the population in the same period. Coverage has expanded, from 84% to 92%, but costs continue to increase incessantly, and operational complexity has reached a breakdown. Even with better coverage, public confidence in the system is decreasing: only 36% of Americans see the quality of the United States health system in line with paid costs, while 54% qualifies it unfavorable, according to recent surveys.
We have achieved broader access. However, we still have to optimize the system.
The load under the surface
While doctors and patients experience visible friction of care (delays, denials, documentation, the underlying load is operational. Manual processes still promote some of the most critical interactions in medical care.
- Prior authorization processes can take more than 35 minutes at request. On a scale, a great health plan can process the authorizations of 80,000–100000 daily.
- Medical record reviews, appeals and complaints and claims processing require the human interpretation of unstructured data: faxes, handwritten notes, PDF.
- The review of the attention gap and the risk and quality evaluations often are left behind the decision making in real time, reducing their effectiveness in the value models based on the value.
And this is not limited to payers. Hospitals and health systems are affected by similar challenges.
- Teams must abstract data manually for clinical research or quality reports.
- Applications from previous authors must begin and track with little visility in the state of the payer.
- Multidisciplinary care equipment is allowed to coordinate by email, EHR notes or spreadsheets, or resulting in duplication, lost opportunities and clinical exhaustion.
All this consumes fixed resources: more than 25% or the total medical care of the United States is now attributed to administrative costs. Meanwhile, the patient’s experience stagnates, the exhaustion of the personnel increases and the trust is eroded.
Built for increasedism, not transformation
A reason why we do not solve this is that the system was built for a slow change. The ACA, despite all its merits, was a stable regulatory environment and an incremental improvement. He embedded programs such as the Patient -centered Investigation Institute and the National Quality Strategy, but did not anticipate the scale, speed and medical care of complexity would assume in the following decade.
It did not foresee the increase in integrated vertical health conglomerates, private capital in the provision of care or concentration of market power players. Nor did he anticipate how technology-AI, automatic learning, self-service diagnosis and big data would evolve, while the medical care system remained largely analog under the surface.
As a result, ACA focused on coverage on capacity. The bet was that with more people in the system, they would follow operational improvements. But they didn’t get out. Administrative processes were largely dressed, and innovation was suffocated by regulatory precaution and resistance to interested parties.
Today, we have a system that looks digital on the surface but that remains deeply manual in the nucleus.
Irrupation from the top?
Recent political changes add even more urgency. Duration of its Senate Confirmation Hearing, incoming administrator of CMS, Dr. Ir. Mehmet Oz made it clear: the change is approaching.
Oz framed the current health model controlled by “150 people who do not want to change,” a surprising reprimand of entrenched interests. He requested the use of real -time data, Emarter workflows and tools that train patients and doctors. He emphasized the transformation driven by AI in areas such as prior authorization, while warned about his possible misuse and supervision.
Its posture underlines a central tension: we need a change, but the system is not built to change quickly enough. That’s where AI agentic becomes not only useful, but essential.
What is AI Agent?
AI agentic refers to intelligent and specific tasks that can allow complex data, interpret the context, the reason between the criteria and collaborate with humans, all in real time. Unlike traditional automation or rules based bots, these agents do not require thorough programming. They learn, adapt and integrate into existing workflows.
They are not tools. They are co -workers.
This is what they allow throughout the medical care ecosystem.
For health plans:
- Prior authorization: Award of simultaneous guidelines through Carelon criteria, interspersed and internal, generating instant and explainable decisions.
- Care gap review: proactive alerts based on real -time stratification of members.
- Review and appeals of medical records: summary and indexation of clinical narratives of EMR of free text, PDF and images.
- Claims processing: Mark inconsistencies and accelerate resolution with structured and reasoned decisions support.
- Rate program and risk assessment: adaptive coding coincidence, prices models and real -time risk setting metrics.
For hospitals and health systems:
- Clinical Research: Accelerate the selection of cohorts by analyzing unstructured notes for chosen markers.
- Attention Management: Synthesize risk factors and interventions to guide high contact scope.
- Income cycle management: Documentation automation for reviews prior to authorization, appeals and compliance.
- Coordination of care: Dynamic decision summaries, AI for multidisciplinary equipment, with visibility of the latest clinical events, gaps and patient preferences.
All these cases share a topic: reduction of load through intelligent collaboration.
“How” or transformation
So how does Agentic Ai be real?
- Ingest the complexity at scale: these systems are built to process faxes, scanned documents, handwritten notes, EMR outputs and claims files, all once. They do not need perfect data. They thrive in messy ecosystems.
- It comprises the context of health in the treatment of everything as generic content, AI AGENTIC includes the specific terms of health, regulatory requirements and clinical pathways. Not only reads a graph, he knows what is relevant, what is missing and what he means.
- Collaborate with transparency: Each information is traceable to its data source. Each action is auditable. Each recommendation can be explained. This is not the construction of Black-Box AI-UT co-pilot for regulatory degree environments.
- It adapts to policy changes: the regulations that evolve, either under a Trump artificial intelligence system or any future, can be quickly updated to reflect new requirements, avoiding expensive rehabilitation and encoded failures.
- The scales throughout the company integrated in previous authentication, the same infrastructure can be applied to medical reviews, appeals, risk analysis and even clinical research, creating a transformation steering wheel.
Why now?
We can’t expect. The next administration, whoever, will face immense pressure to control costs, simplify the provision of attention and restore public trust. That means a stricter supervision of Medicare’s advantage, possible Medicaid reforms and continuous scrutiny of paying practices.
Health plans and suppliers must be prepared now, not with fear, but with preparation. This provision won comes from more manual hiring or new portals. It will come from systems that think, adapt and support.
Agent AI is not just a technological strategy. It is a resilience strategy.
From capacity to capacity
Medical care has been successful in the expansion of its capacity (more affiliates, more technology, more data, but the raw capacity alone can survive instead of empowering. The critical step is the transition of the mere capacity to the true capacity. We need intelligent and scalable systems that lift burned, illuminate the connections and train health professionals to provide their highest quality care.
The AI agent provides this essential capacity. It transforms the complexity of a burden source into a source of opportunity, changing medical attention from reactive compliance to proactive and insightful care. The real transformation does not occur in the holders: it is developed within the daily workflows of the provision of medical care.
We must stop demanding more effort from a tense system and insist on systems designed to amplify human potential. The future of medical care is not merely digital: it is collaboratively intelligently. The future is agent.
Image: Chinese Yuichiro, Getty Images
Ganesh Padmanabhan is the CEO and co -founder of Automize AI, a pioneer company that allows knowledge workers in regulated industries to have access to safe and reliable solutions. Under its leadership, Autonomize develops co -pilots of AI that organize, contextualize and summarize unstructured health data, reducing administrative burning while allowing decisions based on data that improve the results of patients.
Visionary in Healthcare AI, Ganesh founded Autonomize AI in January 2022 after successful companies in the explainable AI and the aggregation of data. The company serves an impressive list of customers, including the 20 main pharmaceutical companies, Fortune 100 payers and the main value -based care organizations. Autonomize AI is also a founding member of Cancerx, part of the initiative of the president of the United States.
A requested main speaker, Ganesh has appeared in Forbes, Business Insider, Fast Company and other leading publications. It was recognized by Enterprise Management 360 as one of the 10 technology experts that revolutionize AI in 2018.
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