Hospitals are facing significant financial challenges due to rising costs, labor shortages, and an influx of denied claims. A Kaiser Family Foundation study found that insurance companies initially deny 17% of claims on average, even for in-network care. The rate of claims denials by payers rose to 11% in 2022 from 10.2% in 2021, resulting in an average of 110,000 unpaid claims per health system that require additional time and expertise to overturn.
Artificial Intelligence (AI) has the potential to revolutionize denials management by streamlining claims processing, enhancing coding accuracy, and extracting essential information from medical records and payer contracts. AI-powered solutions include selection models for account prioritization, workflow acceleration, improved price accuracy, and predictive trend analysis.
Implementing AI in revenue cycle management (RCM) requires consideration of technical, data, talent, and operational factors. Building effective AI models demands quality data, skilled data scientists, robust platforms, and understanding of evolving regulations. Collaboration with industry-leading RCM innovators can accelerate a health system’s path to stronger financial health.
Large Language Models (LLMs) present both tremendous potential and considerable uncertainty in healthcare. Major players like Google are developing healthcare-specific LLM platforms to mitigate risks. Healthcare organizations are advised to start small, leveraging AI for their most challenging problems, such as patient collections.
Interoperability is critical for successful AI implementation in RCM. The underlying data infrastructure must be capable of routing claim-level, clinical, contract, and operational data to AI models and integrating results into operational workflows. As payers increasingly use AI to process claims, healthcare providers can’t afford to delay adopting AI to benefit their revenue cycle and address the growing challenges in denials management.
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