What’s behind the buzz
Artificial intelligence (AI) is playing an increasingly pivotal role in healthcare revenue cycle management (RCM) and has the potential to fundamentally reshape RCM operations. As organizations strive to enhance efficiency, overcome staffing and other resource shortages and improve patient satisfaction, they are increasingly exploring AI-powered solutions. That makes understanding the language of AI a key prerequisite that not only helps identify opportunities to leverage AI but also to evaluate solutions and vendors effectively.
The basics of AI in RCM
AI, at its core, refers to the simulation of human intelligence processes by machines. In the context of RCM, AI technologies such as Business Rules Engines, Optical Character Recognition (OCR), Robotic Process Automation (RPA) and Machine Learning (ML) are transforming traditional processes. These technologies automate routine tasks, enhance data processing capabilities and improve decision-making.
Key AI tech terminologies and their applications
AI technology types have varying degrees of autonomy within processes and workflows, and AI technologies are layered or stacked as needed to build functionality that refines and adapts the rules. Deterministic logic is based on rules and parameters as programmed, so any decision-making is fundamentally hardwired into the engine. Building on that foundation, task automation takes on the autonomy of a human performing the same types of routine, repetitive tasks. Predictive and extractive AI take autonomy to the next level, enabling machines to extract meaning from unstructured data and extrapolate text into predictive intelligence.
Key AI terms to know
- Business Rules Engine: These engines use deterministic logic to automate decisions and workflows. For instance, they can auto-route accounts to work queues based on predefined criteria and assign claims based on payer logic, streamlining operations and reducing manual intervention.
- Optical Character Recognition (OCR): OCR technology extracts text from scanned and printed documents or forms, digitizing paper-based information. This capability is essential for tasks like extracting demographics or codes from faxes, enabling seamless data integration into digital systems.
- Robotic Process Automation (RPA): This form of task automation mimics human actions in systems, such as clicking, copying and navigating websites. It automates repetitive tasks like eligibility lookups, freeing up human resources for more complex activities and reducing errors. Next-level automation, known as iRPA (RPA + AI), adds machine learning and natural language processing to RPA bots for smarter data routing or decision-making.
- Machine Learning (ML): This technology analyzes historical data to identify patterns and make predictions. In RCM, it can predict patient propensity to pay or flag likely denials, allowing organizations to proactively address potential issues.
- Natural Language Processing (NLP): NLP understands and generates natural language, facilitating tasks like drafting appeal letters or summarizing clinical records. This technology enhances communication and documentation processes, improving overall efficiency.
- Large Language Models (LLM): A subset of NLP that understands and generates natural language at scale. As generative AI tools, LLMs can be used to draft appeal letters or summarize clinical records.
- Generative AI: A model architecture used to mimic human creativity and generate text, images, audio and video. It has a very high degree of autonomy.
- Agentic AI: A linchpin of process automation that can act independently, make decisions and respond to varying conditions to produce process results.
- Artificial General Intelligence (AGI): A type of AI that would match or surpass human capabilities across virtually all cognitive tasks. AGI systems can generalize knowledge, transfer skills between domains and solve novel problems without task-specific reprogramming.
Identifying opportunities for AI in RCM
AI offers numerous opportunities to enhance efficiency and accuracy in RCM. By automating routine tasks and providing predictive insights, AI can significantly simplify and shorten revenue cycle processes. Mastering AI terminology empowers healthcare organizations to make informed decisions and leverage AI effectively. By understanding the capabilities and applications of AI technologies, organizations can unlock new opportunities for innovation and efficiency in the revenue cycle.
With hundreds of billions of dollars worldwide invested this year alone, AI technology is quicky becoming ubiquitous, at least in marketing materials and sales pitches. But not all AI is created equal, and in early markets seeking market share, hype often exceeds benefit and ROI. Healthcare providers would be wise to recall the basics of caveat emptor when considering AI-powered solutions and vendors and be thorough in their evaluation criteria and methodologies.
R1 RCM is committed to innovation and leadership in AI-driven RCM solutions. By embracing AI and understanding its potential, healthcare organizations can position themselves at the forefront of technological advancements, ensuring a more efficient and effective revenue cycle.
