How to Improve Payment Integrity with Artificial Intelligence and Machine Learning

October 18, 2023


Advances in artificial intelligence (AI) and machine learning (ML) are bolstering payment integrity efforts, bringing greater transparency and control over claims processing, and ensuring more accurate and fair payment outcomes. AI and ML enhance insight and speed up claim reviews, helping identify and prevent fraud, waste, and abuse while lowering costs and improving patient care.
AHIMA hosted a discussion with CERIS and Amazon Web Services (AWS) on the potential of these technologies and how they can be used in tandem with the human touch to transform healthcare claim processes. This discussion also explored keys to successful implementation and how organizations can determine return on investment (ROI).

Leveraging AI and ML to enhance the claims process

AI and ML are having a significant impact on healthcare claims payment adjudication and have the potential to revolutionize processes and improve efficiencies and accuracy. Claims adjudication has historically relied on the manual review of medical coding, treatment plans, billing, and other relevant information, which are time-consuming and error-prone processes ripe for automation.

AI and ML applications can help automate these workflows and streamline the payment process, reducing errors, accelerating payment cycles, and minimizing administrative costs. AI and ML also have the potential to enhance revenue cycle management by analyzing historical data to predict payment patterns, optimize billing strategies, and improve overall financial forecasting. These things can be accomplished by applying AI and ML algorithms to quickly analyze vast amounts of historical data such as medical coding, patient records, and billing history to identify patterns, detect anomalies, and make accurate predictions.

Keys to successful implementation

Successful implementation requires the early identification of all key stakeholders, communication, and education. Getting buy-in from all stakeholders is essential. As with all information technology projects, adopting AI and ML must align with the organization’s business strategy.

Data is key. Having sound data governance, management, and architecture is crucial to getting the maximum value from AI and ML. Data governance ensures data integrity, enabling the review of vast amounts of data at high speeds, reducing duplication and expediting claims reviews. There is a steep learning curve, however, and a continued challenge in the
industry market to find, recruit, and retain skilled professionals.

Many organizations choose to start off their AI journey with a quick win and create a roadmap to deliver more complex projects over time. To get started, identify a pain point or where staff spend a significant amount of time doing manual processes. A common rule of thumb is to look for AI technologies that can deliver an improvement of at least 20% in the first use case. From there, build a roadmap to implementation in phases, starting with the low hanging fruit and adding on more complex use cases over time. It’s about getting a quick win on the board and building momentum for bigger projects. The biggest downside is the amount of time required for managing the business process change that comes
with any significant upgrade to a core business process. This is why it is important to make sure the first win is big enough to justify the time spent managing the change.

Let's partner to a clear path to greater accuracy and cost savings.

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