Artificial Intelligence (AI) holds immense potential to transform Revenue Cycle Management (RCM) by improving administrative efficiency, decision support, and patient engagement.
AI-driven solutions enable the proactive prediction and management of denials through the analysis of claims data, allowing for early detection of shifts in payer behaviors and facilitating timely interventions to prevent denials.
What contributes to the success of AI in RCM?
Unlocking AI's full potential hinges on executing a meticulously planned strategy. Four fundamental components, or pillars, are crucial considerations for healthcare organizations embarking on a successful AI launch:
- Use Cases: Use cases form the cornerstone of an effective AI strategy. Identifying specific problems to solve ensures that AI initiatives are aligned with organizational objectives. Use cases include automated coding and billing, claims denial prediction, appeal letter generation, and forecasting and optimization.
“It really starts with use cases,” says Spencer Allee, Chief AI Officer at Aspirion. “What are the consistent and repeatable demands, challenges, and steps? How can these be transitioned into problem statements? It is critical to myopically understand what problems you are actually trying to solve and turn these into well-defined use cases. It’s remarkable how often somebody builds tech without a clear understanding of the problem that they’re solving. And then often, that tech ends up not really getting the traction that’s needed.” - Data: Robust data is indispensable for developing reliable AI solutions. Various data types, including claim-level data, clinical data, payer policies, and metadata, fuel AI algorithms, enabling accurate predictions and continuous improvement.
Providers should weigh the worth of their institution's data against broader datasets accessible through partnerships and vendor relationships. Combining data from multiple providers under the same health plan in a region can significantly enhance its value compared to individual datasets. Payers aggregate data across all types of providers, care settings, plans, and geographies. Providers should also explore leveraging a larger data lens—dormant data within larger datasets to extract predictive insights through AI and machine learning (ML) workflows, ensuring returns while managing data release within their comfort level. - Platform: A strong platform is essential for developing, deploying, and managing AI solutions effectively. Key platform features include unified data access and management, intelligent workflow automation, predictive analytics and forecasting, and customized insights and reporting. Think of the platform as the engine, where the use case and the data can be effectively unified.
- Talent: Accessing expert talent, especially data scientists, is vital for a successful AI strategy—though challenging. While roles like RCM operations experts and learning specialists are more accessible, the demand for data scientists is high due to their significant impact on designing effective solutions and extracting optimal value from data. Seamless collaboration among data scientists, engineers, and RCM operators is essential for innovation-driven strategies.
All four pillars—use cases, data, platform, and talent—must work together synergistically to unlock the full power and potential of AI. Merely building the platform, for instance, is insufficient without rich data, well-defined use cases, and expansive access to industry-leading talent.
“It’s like building a great engine and you don't have any fuel for that engine, which is the data. And you don't have a race to put it in, which is the use case, and you don't have a driver—the talent,” Jim Bohnsack, Chief Strategy Officer at Aspirion, explains. “Yes, you can just build the engine, but it doesn't do any good if it doesn't get you anywhere.”
Before embarking on an AI strategy, providers should focus on realistic goals and timelines to avoid overwhelming tasks. Carefully consider the following questions while being mindful not to attempt to "boil the ocean.”
- What are the critical business challenges that require addressing?
- In what ways can AI contribute to solving these challenges?
- What approach will be taken to acquire or develop the AI solution?
- How will success be defined and measured, and what is the timeline for evaluation?
Often strategic partnerships, with industry leaders like Aspirion, are vital for success. Partnering with organizations that possess direct data access, well-defined use cases, robust platforms, and top talent significantly accelerates progress, reduces risk, and allows for more impactful results.
Rather than if or when, RCM leaders must focus on how, how fast, and with whom to adopt AI. Each healthcare organization should tailor its approach to suit its specific circumstances and priorities. Committing to a roadmap and investment is crucial to begin this journey. AI will increasingly become a staple in healthcare RCM, like in other industries, to optimize revenue cycle operations, enhance financial performance, and ultimately deliver improved patient experiences.