Workflow acceleration
AI can also be used to simplify workflows and accelerate time to action by automating the extraction of essential information from claims, medical records, contracts, and guidelines, and then applying that information to accelerate activities. For instance, AI can generate draft appeal letters, minimizing manual effort, reducing errors, and improving the overall workflow for improved revenue outcomes.

Improved price accuracy
AI can be invaluable to RCM teams to unlock the value of unstructured data in medical records, managed care contracts, and other sources by identifying reimbursement rate discrepancies and ensuring contract compliance. This helps prevent denials and underpayments and arms providers with critical information which can help ensure accurate reimbursement in accordance with negotiated agreements.

Predictive trend analysis
As denial rates continue to climb, payers are also making changes to coding policies, creating more complexity around submitting claims and posing ever more challenges to short-handed provider organizations. AI can help by identifying and predicting trends in denials and payments to enable RCM teams to make operational updates to avoid denials, and when denials do occur, add the ability to predict which denials are more or less likely to be overturned so that RCM teams can focus their limited resources more effectively.

Considerations for AI-powered denials management implementation
When incorporating AI and automation into RCM, healthcare organizations will face a myriad of technical, data, talent, and operational considerations.

Building effective AI models is tough. It requires an enormous amount of quality data that can be used to train and mature models. It requires hard-to-find data scientists with sophisticated talent who understand how to leverage data in concert with isolating meaningful use cases, such that machine learning can establish meaningful learnings. It requires underlying platforms and engineering strength that allow models to be effectively deployed. It requires a sophisticated understanding of evolving standards and regulations to ensure ethical and permitted usage. Leveraging industry-leading RCM innovators who have already proven success in this emerging field can accelerate a health system's path to stronger financial health.

Large Language Models (LLMs) or foundational models represent a groundbreaking innovation, presenting both tremendous potential and considerable uncertainty. The regulatory landscape is catching up with the technology, and the rapid evolution of the technology itself introduces uncertainties. Collaborating with strategic partners skilled in this becomes a vital strategy. Notably, major players like Google and other cloud providers are developing healthcare-specific LLM platforms, contributing to risk mitigation in this evolving landscape.

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