By Amy Amick

At a time of rising costs, labor shortages, and operating margins still below historical levels, many hospitals are simply not in a position to absorb any loss of revenue. Yet that’s exactly what is happening when it comes to managing the influx of denied claims as part of the revenue cycle management (RCM) workflow.

Hospitals are grappling with a surge in claims denials, marked by an increase in the average dollar value per denial, extended resolution times, and reduced yield per claim across all payer types. A Kaiser Family Foundation study on Affordable Care Act (ACA) health plans found that, on average, insurance companies initially deny 17% of claims, even for in-network care. Prior authorization denials have also risen, as identified in commercially insured Medicare Advantage plans.

For hospitals still struggling to dig out from the financial hole left by COVID-19, the timing couldn’t be worse. According to an analysis from Crowe, the rate of claims denials by payers is frustratingly high, rising to 11% in 2022 from 10.2% in 2021. For the average health system, that’s 110,000 unpaid claims that require additional time and expertise to fight to overturn. The situation is even worse with aging AR. The same report found the proportion of hospital accounts receivable that have aged beyond 90 days has been trending upward and was at 37% as of August 2022. Compounding these challenges is a critical shortage of talent and expertise needed to navigate the complex denials landscape. According to an Experian Health survey, 100% of revenue cycle leaders recognize that the widespread healthcare workforce shortage significantly impedes payment collections.

Is AI-powered denials management a game-changer?
AI holds the potential to revolutionize the denials landscape, and its impact is already evident. Through the use of artificial intelligence, healthcare providers can streamline claims processing, enhance coding accuracy, and extract essential information from medical records and payer contracts to tackle the root causes of denials.

While the expertise of seasoned revenue cycle professionals remains crucial for denials resolution and prevention, the integration of AI and automated workflows empowers these professionals to operate at peak efficiency and effectiveness resulting from the following:

Selection models
In the context of AI, selection models are designed to choose or classify specific items or entities based on certain criteria. These models are a type of machine learning that can help automate the prioritization of accounts to increase yield.

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