Automation and artificial intelligence (AI) in revenue cycle management (RCM) have numerous real-world applications as healthcare organizations seek to implement advanced technology and tools. This first article in our three-part RCM automation series explores the automation continuum and the various forms of automation that are reshaping the healthcare industry.
Discovering the Automation Continuum
The automation continuum can be categorized across four progressive stages of increasing complexity and capability: basic automation, advanced automation, intelligent automation, and generative AI.
Basic Automation emphasizes rules-based processes designed for repetitive tasks, which operate under predefined instructions. This foundational level of automation performs simple, routine operations, reducing manual workload and minimizing errors. An illustrative example in healthcare is the automated claim status query process, where a standard transaction prompts a predetermined response. For example, healthcare providers submit an ANSI standard claim status transaction to payers and receive a predefined response, typically a numeric code that translates into a status definition, such as “paid” or “pended”. This basic automation helps streamline routine operational tasks, enabling staff to focus on more complex issues.
Advanced Automation incorporates more complex algorithms and machine learning (ML) to offer predictive analysis. By analyzing historical performance and other data sources, these systems forecast future scenarios and outcomes to enable proactive decision-making. An example might be using machine learning models to predict claims that are likely to be denied. This allows providers to identify high-risk claims before submission and take corrective actions, such as conducting a comprehensive pre-submission review.
Intelligent Automation integrates natural language processing (NLP) and unstructured data, enabling human-like reasoning and the ability to navigate ambiguity. This level of automation can manage complex interactions, such as those involved with advanced chatbots, and provide nuanced responses far beyond the capabilities of basic decision trees. Artificial intelligence (AI) opens new avenues for innovation in healthcare, such as applications that can interpret physician notes, analyze patient histories, and provide more accurate diagnostic and treatment pathways using sophisticated algorithms. These more advanced forms of automation using ML, deep learning (DL), and NLP models can recommend the next best actions and help prevent denials.
Generative AI creates new, original content and solutions based on the input data. Unlike traditional AI that reports observations, generative AI can generate new, creative solutions. This leap forward enables automation systems to not just follow predefined rules or make predictions based on past data, but to offer new answers and approaches to solve complex problems. Generative AI could revolutionize patient care by designing personalized treatment plans and predicting patient outcomes, potentially leading to significant efficiency gains and innovative breakthroughs.
As healthcare organizations navigate the spectrum of automation—from basic systems to generative AI—there is considerable potential for more efficient, insightful, and predictive revenue cycle management. Embracing these technologies can lead to significant improvements in operational efficiency, patient satisfaction, and overall financial performance. Contact us to discuss how we can help your organization embrace the transformative capabilities of automation, and watch for our next article in this series, where we will examine the current state of AI in RCM and explore future applications of these technologies.
Patrice R. Wolfe
