Physicians who used DAX, a popular agentic AI in healthcare solution, reported spending 24% less time on notes and entertained 11.3 additional patients a month. Today, the data generated in healthcare is overwhelming and only 3% of healthcare’s data is effectively used. This creates a pressing demand for agentic AI solutions that can process and analyze large amounts of data and make decisions while letting humans focus on more cognitive tasks.
What is Agentic AI in Healthcare?
A major challenge in data processing in healthcare is its multimodality. Data in healthcare exists in diverse formats like handwritten notes, EHR, scans, X-ray images, USG videos, and telemedicine recordings. Agentic AI understands multimodal data from diverse sources. They can analyze, deduce, and infer solutions by synthesizing complex information. But this is not all, they can develop strategies to address problems, considering multiple variables and potential outcomes and execute them in real-world scenarios. At its core, agentic AI operates on three fundamental principles:
- Autonomy in decision-making within defined parameters
- Continuous learning from interactions and outcomes
- Goal-oriented behavior with the ability to adjust strategies
The Data Overload Challenge in Healthcare
A Statista report predicts that by 2025, the world will generate approximately 180 zettabytes of data, with healthcare accounting for over one-third of it.
Despite this massive data production, only 3% of healthcare data is effectively utilized due to outdated systems incapable of handling large-scale, multi-modal data. As a result, clinicians are often burdened with manually analyzing extensive datasets to extract insights, leading to missed opportunities for timely and effective care.
This issue is rapidly escalating. According to the National Institutes of Health (NIH), medical knowledge now doubles every 73 days, particularly in fields such as oncology, cardiology, and neurology. The overwhelming surge of information places significant pressure on healthcare providers, requiring them to make accurate decisions within increasingly constrained timeframes. Let’s take a look at the trifecta of problems arising from data overload in a typical oncology department.
Cognitive Overload
Oncologists and other healthcare providers must manually review and interpret test results, detect abnormalities, and prioritize follow-up actions without intelligent assistance, increasing their mental workload. The anesthesiologist must manually research patient medications and allergies, leading to additional cognitive strain.
Care Plan Orchestration
The oncology nurse must manually send test requests and coordinate with different departments (imaging, surgical), instead of having automated systems handle scheduling and notifications. The absence of streamlined processes for care coordination leads to delays in treatment and impacts patient care.
System Fragmentation
Lack of integration between systems means that the MRI technician cannot easily access the patient’s medical history, forcing them to manually seek expert advice about critical factors (e.g., pacemaker compatibility with MRI). Disconnected systems and manual interventions create inefficiencies and increase the risk of errors, further delaying timely care.
How Agentic AI in Healthcare Improves Care Delivery?
Agentic AI in healthcare intends to provide sufficient care time for patients by automating multiple operations.
Claims Processing
In the complex world of healthcare insurance, Agentic AI is transforming claims processing into a seamless and efficient experience. By intelligently reviewing documentation, spotting discrepancies, and flagging issues for human intervention only when needed, these AI agents ensure claims are processed with accuracy, fairness, and full regulatory compliance.
But their impact doesn’t stop there. These smart agents optimize workflows, slash approval times, and drastically cut down on administrative burdens. By harnessing real-time data and predictive analytics, they empower clinical teams to focus on what truly matters—delivering quality care.
AI Agents maintain a detailed memory of claim histories for quick and informed decision-making. They seamlessly connect with systems to verify eligibility in real time. With smart planning and execution, AI agents streamline every step, ensuring claims move smoothly through the pipeline.
Care Coordination
Fragmented systems often create barriers to effective care management, leaving providers struggling to connect the dots across electronic health records (EHRs), care management tools, and scheduling systems.
Agentic AI bridge the gaps, collecting and synthesizing data to streamline workflows. By predicting which high-risk patients require immediate attention, they empower care teams to act proactively, reducing preventable hospital readmissions and improving patient outcomes.
Specialized AI Agents retrieve information from diverse systems, consolidating it into a single, easy-to-navigate view. They automate care coordination, ensuring seamless communication and task prioritization across teams. Leveraging predictive capabilities, they help identify patients who need urgent care, enabling timely and effective interventions. By eliminating silos and automating coordination, Agentic AI transforms care delivery into a unified, efficient, and patient-centered process.
Prior Authorization
Navigating the maze of prior authorizations can be time-consuming and frustrating for healthcare providers and customers alike. Agentic AI steps in to transform this process, autonomously managing authorizations with precision and speed.
By evaluating resource usage, eligibility criteria, and required documentation, these intelligent agents anticipate potential bottlenecks, slashing review times by up to 40%. Resulting in a smoother, faster process with complete transparency for all parties involved.
The agent pulls patient and billing data from various systems with ease. It applies complex rules to verify authorization eligibility in seconds. With minimal manual input, the agent handles the entire process, ensuring faster approvals and fewer delays.
Data Reconciliation
Reconciling payment data from claims to provider payments has traditionally been a labor-intensive and error-prone process. But AI agents are changing the game, automating this tedious task with remarkable precision.
By cross-referencing claims, provider records, and financial transactions, these agents ensure payments are accurately matched. They not only reduce manual effort but also enhance accuracy by flagging discrepancies for swift resolution—saving time and minimizing errors.
The agent connects to financial databases, claims systems, and provider records in real time. It identifies inconsistencies in payment amounts, timelines, or other critical details with ease. By highlighting issues instantly, the agent enables quick fixes, ensuring smooth payment workflows.
EHR Management
Managing electronic health records (EHRs) can be a daunting task, but Agentic AI is transforming the process into a seamless, efficient system. By consolidating data from multiple sources, automating routine tasks like chart updates and follow-ups, and ensuring records stay accurate and up-to-date, these intelligent agents give clinicians real-time access to well-organized patient information.
What’s more, Agentic AI enhances interoperability, allowing seamless data exchange between systems, which directly improves care delivery and coordination.
The agent integrates patient information from various sources into a single, cohesive view. It flags missing or inconsistent data to ensure records are complete and reliable. Routine administrative tasks are automated, reducing workloads and boosting efficiency for healthcare providers.
Personalized Patient Care
Gone are the days of one-size-fits-all healthcare. Agentic AI is revolutionizing personalized patient care by diving deep into each patient’s medical history, genetics, lifestyle, and ongoing treatments to craft care plans tailored to their unique needs.
These intelligent agents don’t just stop at creating individualized care plans—they predict potential health risks, recommend preventive measures, and keep patients engaged with personalized communication and timely reminders.
The agent synthesizes a wide range of patient data to design customized care plans. It tracks patient progress and adjusts care plans as needs evolve. It tracks patient progress and adjusts care plans as needs evolve.
The Future of Healthcare: From Agentic AI to AGI
As agentic AI systems become more sophisticated, healthcare may open up to the potential of artificial general intelligence (AGI). AGI refers to machines that possess the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to humans. A study published in Nature highlighted that AI systems could match or even surpass human radiologists in detecting breast cancer, achieving an accuracy rate of 94%. While AGI remains a distant goal for the AI community, yet healthcare and life sciences might be one of the first fields to approach this threshold.
Understanding biological systems demands dynamical analysis and decision-making capability just what AGI sounds like. A day will come when AGI will continuously monitor a patient’s health status and proactively suggest lifestyle changes or alerts medical professionals about potential issues before they escalate. As AGI systems learn from an ever-expanding pool of patient data, they could facilitate the development of personalized medicine approaches on a larger scale. Agentic AI that can navigate the complex space of understanding biological models and phenomena can eventually make decisions, learn from failures, and propose innovative solution eventually maturing towards AGI.
