The AI Noise Problem
With what may be one of the fastest technology adoption curves since the wheel, artificial intelligence (AI) stands enigmatically today as both a fountain of innovation and a firehose of confusion. As AI’s promise grows, responsible vendors must cut through the marketing buzz and help revenue cycle leaders separate fact from fiction.
The past year has seen a frenetic pace of AI adoption. A report by the Healthcare Information and Management Systems Society (HIMSS) shows a majority of medical workplaces surveyed have already been using AI of some kind for at least ten months, with only a third using AI for six months or less.
While this fast adoption is fueled in part by the overwhelming need to reduce complexity, any adoption of new technology poses risks that must be considered and evaluated. That’s why it is important to seek partners committed to ethical, responsible AI development and application. With the AI bandwagon gaining speed and passengers, the sheer number of vendors and solutions vying for attention can make it challenging to objectively evaluate options.
“To cut through the market noise and achieve meaningful benefit, providers need to focus on the orchestration of AI systems across the revenue cycle that are context-aware and integrated. This approach enables seamless processes and collaboration across workflows, ensuring that AI solutions are not just isolated tools strewn about the workbench but are part of a cohesive solution set that enhances process efficiency and financial outcomes.“
Mark Sithi, senior vice president of Product at R1.
The Difference Between Automation and AI
Understanding the distinction between automation and artificial intelligence is important when considering these strategic tech investments.
- Automation executes repeatable, rules-based tasks according to predefined parameters. It excels in handling routine processes such as checking patient eligibility before appointments, routing and flagging denials based on specific codes and posting payments based on electronic remittance data.
- Artificial intelligence, on the other hand, brings a layer of intelligence and adaptability that automation lacks. AI systems can learn patterns, make predictions and adapt their approach over time. They can generate custom appeals letters for different payer types, place agentic payer phone calls and automatically generate payer-specific bill edits on claims.
Despite their differences, automation and AI do have some things in common. They both aim to improve efficiency at scale, require governance and monitoring to ensure performance and both can work independently or in tandem. But the ability to detect discrepancies, adapt to new information and replicate human decision making sets AI apart from traditional automation.
The keystone characteristic of AI, then, is its capacity for learning, interpreting information and making decisions much like a human. This allows AI to handle more complex tasks that require a nuanced understanding of data and context. While automation is effective for streamlining straightforward, repetitive tasks, AI’s adaptive capabilities make it ideal for tackling challenges that involve variability and require ongoing learning.
The Limitations of Point Solution AI
Point solution AI often falls short of delivering the comprehensive benefits that organizations seek because isolated AI agents typically perform narrow tasks such as automating prior authorizations or streamlining claims processing. While they can offer short-term efficiency gains, they often replicate the same siloed structure that has long plagued the healthcare revenue cycle.
The limitations of point solutions become most apparent in the broader context of revenue operations. Each AI agent operates in a vacuum, optimizing its own task with no understanding of the interconnected nature and purpose of the entire system. This can result in disconnected processes, disruptions and inefficiencies, as isolated solutions can’t communicate and collaborate effectively across different stages of the revenue cycle.
The Role of Agentic AI Orchestration
Unlike point solutions that operate in isolation, agentic AI orchestration involves connecting a network of intelligent agents that work collaboratively across the entire revenue cycle. This approach transforms the way tasks are managed, moving from linear, step-by-step processes to a dynamic, non-linear orchestration with AI always ‘sensing’ new or updated information to act upon. Agentic orchestration enables intelligent agents to make independent modeled decisions based on learned behaviors, contextual reasoning, dynamic adaptation, shared goals and real-time data.
By enabling systems to complete tasks non-linearly, providers can achieve greater scalability and efficiency. Agentic orchestration enhances the ability of systems to reason through complex data patterns, understand context and activate other agents across the ecosystem. This interconnection ensures that accounts are resolved with greater precision, speed and consistency.
When evaluating AI partners, it’s essential to look beyond surface-level promises and delve into the specifics of what each vendor offers. The right AI partner should not only provide advanced technology but also demonstrate a deep understanding of the healthcare revenue cycle.
“Selecting AI solutions is not just about implementing technology,” says Sithi. “It’s about ensuring that these tools are grounded in real-world applications and supported by experts who understand the nuances of healthcare finance and revenue operations.”
When evaluating AI vendors, consider these key characteristics:
- Vendor experience and expertise: Vendors with established scale and scope across the provider environment, plus a proven track record of successful projects and a deep understanding of the unique challenges and requirements of the industry, present less risk.
- Security and compliance: Vendors must meet all data privacy requirements, such as HIPAA, and have robust measures in place to protect sensitive. Insist on a strong focus around all things related to regulatory compliance, organizational integrity and personal accountability.
- Integration capabilities: Solutions should seamlessly integrate with existing technology stacks, especially electronic health records (EHR) and payer connectivity systems. It’s also important to evaluate the support and training provided.
- Pricing and support: Vendors should be transparent about their pricing structure and the total cost of ownership, including licensing, implementation, support and training.
By thoroughly evaluating AI vendors, revenue leaders can make informed decisions that align with their strategic goals and ensure the successful implementation of AI solutions that drive real value and improve operational efficiency.
Parting Thoughts
As AI continues to revolutionize administrative operations, forward thinking revenue leaders remain committed to innovation and collaboration to drive complexity out of the system. By leveraging the latest AI technologies and fostering a culture of continuous improvement, providers can overcome major process obstacles and achieve remarkable operational progress. The future holds immense potential for improvement, and with dedication and expert guidance, healthcare providers can turn these possibilities into reality.
