What is Agentic AI & How Will it Change RCM?

AI is rapidly transforming various industries, and healthcare is no exception. Within healthcare, the application of AI in revenue cycle management (RCM) is particularly significant. Agentic AI is emerging as a powerful technology that goes beyond traditional automation, promising to revolutionize how healthcare providers handle their financial operations.

Agentic AI Explained

Agentic AI is a type of AI that is designed to operate autonomously, making decisions and taking actions with minimal human intervention. It goes beyond simply following pre-programmed rules, instead adapting to changing circumstances and learning from its experiences. This makes it different from traditional AI systems, which are often based on fixed models and require constant human direction.

Agentic AI operates through four key stages:

1. Perception: It gathers data from various sources such as databases and digital interfaces. In healthcare, this could involve analyzing patient records, medical reports, and financial data.
2. Reasoning: The AI processes the data to understand the context and identify problems. This involves using large language models (LLMs) to interpret instructions and coordinate specialized models.
3. Action: Based on its understanding, the AI decides on the best course of action. This could include tasks like processing claims, verifying eligibility, and managing prior authorizations.
4. Learning: The AI improves its performance over time by analyzing the results of its actions and adapting to new situations. This is done through a feedback loop where data from interactions is used to enhance the models.

The ability to operate autonomously, adapt to change, and learn over time is what distinguishes agentic AI from traditional automation systems, such as Robotic Process Automation (RPA). Traditional RPA systems follow pre-defined rules and struggle with complexity. Agentic AI, on the other hand, can handle unstructured tasks that require decision-making and problem-solving abilities.

Agentic AI vs. Generative AI

It is also useful to distinguish agentic AI from generative AI (GenAI). Generative AI is focused on creating new content, such as text, images, and code, while agentic AI is focused on action and decision making. Generative AI requires human input to define context and goals, while agentic AI is designed to operate independently with minimal human intervention. While generative AI is about creating, agentic AI is about doing. They can be used in tandem to create powerful solutions.

Why Agentic AI? To Solve 4 Core Problems with RCM

The healthcare industry is facing a major challenge with data overload. By 2025, it's estimated that healthcare data will contribute more than one-third of the 180 zettabytes of data generated globally. Yet, only 3% of healthcare data is effectively utilized due to outdated systems incapable of processing large-scale, multi-modal data. This means clinicians spend a great deal of time manually sifting through large amounts of information to derive insights, a process that is time-consuming and prone to error.

Problem #1: Cognitive Overload

Clinicians face a deluge of data and must derive actionable insights under tight time constraints. The sheer volume of information, including patient history, lab results, and medical images, can overwhelm healthcare providers. 

For instance, an oncologist reviewing a patient's Prostate-Specific Antigen (PSA) results must consider the patient’s medications, therapies, procedures, symptoms, medical imaging, and biopsies, all within a short 15- to 30-minute consultation. This is an example of the cognitive burden and the time constraints placed on clinicians.

Problem #2: Fragmented Systems

Patient data is often scattered across various platforms and systems. This lack of interoperability can lead to delays and inefficiencies in care delivery. 

For example, a patient's blood test results might be in one system (EMR) while their medication history, existing conditions, and prior reports and images are stored in other systems. A physician must manually cross-reference this data instead of having an integrated view. This can lead to a crucial correlation, such as a high cholesterol level combined with an elevated blood glucose level, being missed

Problem #3: Manual Processes

Many tasks in healthcare, such as scheduling tests, requesting authorizations, and processing claims, are still performed manually. This reliance on manual processes not only slows down care delivery but also introduces the potential for errors. 

For instance, after a PSA test, results are entered into an electronic medical record system, and an oncologist must manually check for abnormal levels and request further tests. These requests may be delayed without an intelligent system to flag urgency and automate prioritization. In addition, the oncology nurse manually sends requests to the imaging unit and surgical department, instead of the systems automatically sending requests and handling scheduling. Also, the MRI technician must manually seek expert advice about individual factors such as a pacemaker, which can interfere with the MRI machine.

Problem #3: Inflexible Automation

Robotic Process Automation (RPA) systems, while intended to automate repetitive tasks, are often rigid and break easily, requiring manual intervention and ongoing maintenance. RPA workflows follow exact rules and steps and struggle with complexity, unable to adapt to changing situations. 

For example, RPA may work well for claims that fit a precise template, but they fail when they encounter exceptions or unstructured data. Moreover, setting up RPA can take months, even for simple automations, and they are prone to breaking easily

These issues lead to delays in care, increased administrative burdens, and higher operational costs. This is where Agentic AI offers a significant improvement.

How Will Agentic AI Transform RCM?

Agentic AI offers a more efficient and reliable solution for automating RCM workflows. It can streamline processes, reduce errors, and free up healthcare professionals to focus on patient care. Unlike traditional RPA systems, agentic AI can adapt to changing environments, learn from new information, and make complex decisions, all while requiring minimal human supervision.

Here are some specific examples of how agentic AI will transform RCM:

• Claims Processing: Agentic AI systems can automate the entire claims process, from initial filing to final payout. They can review documentation, detect discrepancies, and flag issues for human intervention when necessary. This leads to faster processing times, fewer errors, and improved customer experience.
• Prior Authorization: Agentic AI can manage prior authorizations autonomously by evaluating resource usage, eligibility criteria, and required documentation. This reduces review times and provides greater transparency for healthcare providers and payers.
• Data Reconciliation: AI agents can cross-reference claims, provider records, and financial transactions to ensure accurate payment matching, reducing manual effort, improving accuracy and flagging discrepancies.
• EHR Management: Agentic AI streamlines electronic health record (EHR) management by consolidating data from multiple sources, keeping records updated, and automating routine tasks such as chart updates and follow-ups. This helps clinicians have real-time access to accurate and organized patient information.
• Eligibility Verification: Agentic AI systems can automate the complex process of verifying patient insurance eligibility with greater speed and accuracy.
• Denial Management: AI agents can analyze denied claims to determine the reasons for denial, and suggest and even implement corrective actions.
• Personalized Patient Care: By analyzing a patient’s medical history, genetics, lifestyle, and ongoing treatments, agentic AI can create tailored care plans, predict potential risks, and recommend preventative measures. This personalization can lead to improved patient engagement and adherence to care plans.
• Decision Support: Agentic AI can provide valuable recommendations to healthcare providers by analyzing real-time data to guide them toward the most effective treatment approaches.

Challenges of Implementing Agentic AI in Healthcare: For Your Consideration

While agentic AI offers significant benefits, there are also challenges to consider when implementing this technology in healthcare:

• Data Privacy and Security: Patient data confidentiality is paramount. Stringent regulations like HIPAA must be followed to ensure data security.
• Integration Costs: Deploying agentic AI systems can require investment in infrastructure and training.
• Ethical Concerns: Questions about accountability and decision-making autonomy remain areas of concern. It’s critical to maintain human oversight and ensure systems are transparent.
• Adoption Resistance: Healthcare professionals may be hesitant to trust AI systems, highlighting the need for clear communication and training.

Why Use Magical Agentic AI in Your RCM?

Magical is leveraging the power of agentic AI to transform RCM by automating repetitive workflows into automations that can run without constant oversight. Magical's AI agents understand actions once and create powerful automations that can run infinitely. Unlike RPA, which struggles with nuanced and complex workflows, Magical's AI agents can adapt to changes and handle edge cases automatically.

Key aspects of Magical’s agentic AI capabilities include:

• Automating Complex Workflows: Magical can automate entire processes from start to finish without needing to code. It can move data between systems, navigate forms, and submit information on autopilot.
• Intelligent Problem Solving: Magical’s AI agents intelligently adapt to changes and infer next steps, making automations more reliable and flexible than traditional rule-based approaches.
• Smart Data Transformation: Magical can automatically move and transform data between apps, handling date conversions, text extraction, and formatting.
• Intelligent PDF Processing: The technology can extract data from any PDF and populate it into online forms instantly, handling medical records and insurance forms.
• AI-Powered Resilience: Magical’s AI agents adapt to changes, handle errors and learn continuously.
• Faster Implementation: Unlike RPA which can take months to set up, agentic AI solutions can be implemented quickly and cost-effectively.

Magical’s technology provides concrete benefits for RCM operations. For instance, tasks that previously took 45 minutes can now be completed in 15 minutes, with no errors. It can also automate tasks related to medical coding, claims decisioning, and eligibility verifications. This allows front desk staff to complete their tasks more quickly.

Magical integrates with existing healthcare web tools, making it easy to implement within existing workflows. It is used by many innovative companies including healthcare organizations and is saving them significant time and resources.

The Future of Agentic AI in RCM

Agentic AI is poised to become an essential tool in healthcare. As AI systems continue to evolve, it’s likely that they will be integrated into more advanced systems for predictive analytics, remote care, and precision medicine. Agentic AI’s ability to streamline workflows, reduce administrative burdens, and improve decision-making will allow healthcare providers to focus on what matters most: patient care.

Agentic AI is not just about automation; it's about augmenting human capabilities to deliver better patient outcomes. By leveraging the power of agentic AI, healthcare organizations can transform their RCM processes, ensuring they are more efficient, accurate, and responsive to the needs of both patients and providers.