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Managing clinical claims appeals is a costly and convoluted function of the healthcare revenue cycle, requiring the time and attention of clinicians and support staff who otherwise could be focusing on higher-priority tasks.

 

Managing clinical claims appeals is a costly and convoluted function of the healthcare revenue cycle, requiring the time and attention of clinicians and support staff who otherwise could be focusing on higher-priority tasks. Introducing generative artificial intelligence (AI), or GenAI, into the claims appeals process can reduce the administrative burden on clinicians and staff and improve the efficiency and accuracy of appeals. 

 

With a more streamlined appeals process, healthcare organizations have the potential to increase reimbursement and enable more seamless interactions with payers.

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From automation to AI in the revenue cycle

AI builds upon the foundation set forth by robotic process automation (RPA)

 

Despite some shortfalls in its capabilities, RPA technology has made great strides in reducing inefficiencies in health systems’ revenue cycles and reducing administrative friction with payers. 
 
Now, organizations are taking the next step by integrating generative AI to optimize revenue cycle performance.

RPA

  • Eliminates human error
  • Automates redundant and repetitive tasks
  • Gathers and manages data efficiently

AI

  • Leverages existing RPA, technology, and security infrastructure
  • Learns and improves over time
  • Makes predictions from its knowledge base

RPA + AI

  • Alleviate the administrative burden on highly credentialed providers
  • Strengthen claims appeals
  • Streamline the revenue cycle

AI and healthcare claims appeals

How does it work?

 

Combining human intelligence with automation, generative AI draws on RPA capabilities and clinicians’ clinical expertise to draft timely claims appeals with less administrative effort from providers.

1

Payer denies claim

2

RPA receives denial and prompts AI to understand the cause

3

RPA securely gathers key info from the electronic health record (EHR)

4

RPA prompts AI to summarize key information from EHR

5

RPA sends summarized information and cause for denial to AI

6

AI drafts appeal letter

7

RPA creates a document
for review

8

Appeal is sent to a human approver for final review

9

The appeal document is sent back to the payer

What are the benefits?

To address rising healthcare costs and complexities, healthcare organizations need solutions that provide value across the organization and to their patients and their families.

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Key considerations

As healthcare organizations consider AI use cases, an enterprise strategy is essential to ensuring they have the infrastructure, governance, and people to support and seamlessly integrate the technology.

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Strategic alignment

Engaging key business functions and clearly articulating the goals of implementing AI, ensuring they align with broader business objectives.
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Data management

Establishing a robust data management strategy focused on quality, storage, security, and governance.
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Ethics

Developing guidelines and policies to address the ethical considerations of AI, such as bias.
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End user design

Prioritizing human-centric design to promote adoption.
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Change management

Investing in enablement and training to articulate what AI is and how to use it safely and effectively.
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Metrics and reporting

Establishing metrics and reporting mechanisms to regularly measure performance and identify improvement opportunities.

Speak with our Experts

Meet our experts
Grant-Baker

Grant Baker

Managing Director

,

Healthcare Performance Improvement, Revenue Cycle

Grant has more than 10 years of healthcare leadership experience in revenue cycle operations, performance improvement and organization redesign.
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James-Hillenmeyer-FINAL

James Hillenmeyer

Managing Director

,

Healthcare Performance Improvement, Revenue Cycle

James has more than 17 years of performance improvement consulting experience across a diverse range of healthcare settings, including academic health centers and large health systems, community hospitals, and physician groups.
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Austin-Pilotte

Austin Pilotte

Managing Director

,

Healthcare Performance Improvement, Revenue Cycle

Austin has 10 years of experience working with healthcare organizations to improve performance, redesign operating models and implement large-scale organizational change. He has expertise across all areas of the revenue cycle and has worked with clients ranging from small physician practices to nationwide health systems.
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Fanny-Ip

Fanny Ip

Chief AI Officer

Fanny has over 20 years of experience guiding institutions in many industries through business transformation, customer experience improvement, and automation maturity.
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