Why AI and Machine Learning Need to Be Applied to Denial Management


Why AI and Machine Learning Need to Be Applied to Denial Management

Although artificial intelligence (AI) has been working behind the scenes of many popular digital products and services for some time, the interactivity, utility, and accessibility of ChatGPT have turned AI into a global phenomenon.

Nearly all industries are rushing to adopt AI and machine learning technologies to cut costs and increase efficiencies, and healthcare is no different. These algorithm-based technologies increase accuracy and automate many time-consuming tasks, thereby enabling employees to focus on other operational responsibilities.

In fact, AI is already widely used in many facets of healthcare. According to the study Artificial Intelligence: How is It Changing Medical Sciences and Its Future?:

Common applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients.

Denial Management Is Currently a Missed Opportunity for AI Adoption in Healthcare

But when it comes to denial management, the adoption of AI and machine learning is far behind many other facets of healthcare. As we cover in The Top Three Challenges In Denial Management, a lack of automation is one of the foundational issues that providers are grappling with. For example, only 38% of hospitals and health systems currently apply any level of automation to denial management, and 62% of hospitals apply no automation whatsoever to denial management.

Considering that another major challenge in denial management is a lack of staff with appropriate knowledge due to time constraints for training and general turnover, it becomes glaringly obvious that AI and machine learning are sorely needed in denial management to protect the provider’s bottom line and financial future.

The Benefits of AI in Denial Management

Avoiding Denials

The first benefit of AI for denial management is keeping claims from being denied in the first place. When applied to claims management, AI can improve the overall efficiency and accuracy of healthcare claims processing, leading both to fewer denials and a more seamless patient experience.

Instead of waiting for denials to occur before taking remedial action, providers can use AI and automation to proactively detect errors and diagnose weaknesses in the claims process for a healthier revenue cycle with fewer denied claims.

Not only can AI and machine learning accurately predict many denials, they can ensure correct data entry, streamline manual processes, and identify denial trends. They can also integrate into billing workflows to prioritize the work queue to resubmit claims.

Enhancing Staff and Workflow Efficiency

While automation benefits team members from time-consuming, process-driven tasks, AI also allows them to perform additional tasks at a higher level. For example, when it comes to processing denials, team members may be working denials in an oldest denial scenario, denial code or reason, payor based list, or even by service level assignments.

Often, they don’t understand the claim’s potential for payment. But an AI technology can identify the highest-value denials and enable staff to focus on reworking those denials first. These benefits not only cut costs but also increase patient satisfaction, resulting in increased retention of those patients.

With the guesswork removed, staff can prioritize denials based on monetary value and likelihood of reimbursement, so time is no longer wasted chasing higher payments that are unlikely to materialize.

Continuous Denial Management Improvements With AI

As AI and machine learning tools improve the accuracy of submissions and the efficiency of processing and appeals, they can analyze the data over time to identify patterns in claims that are denied or rejected down to a payer-specific level. When new claims occur that share similar qualities, the AI tools can flag them for review before submission. Equally important, the adjustments that make these flagged claims successful can be detected and then integrated into future claims.

Seize a Can’t-Miss Opportunity for Claims Denial Management and Automation

Claims denial management is often misunderstood both in its scope and importance to the revenue cycle. As healthcare providers can use AI and machine learning to enhance so many facets of their operations, it is understandable why claims denial management has fallen behind in AI adoption. But understandable or not, continuing this trend is a massive missed opportunity for many healthcare providers. 

AI and machine learning tools can reduce errors on the front end and denials on the back end, increase the success of appeals, build recursive improvements into the process, and greatly reduce the burden on staff. All of these areas are absolutely critical to the revenue cycle and an organization’s overall success.

Using AI to Enhance Your Denial Management 

If you want to leverage AI and machine learning to enhance your own denial management program, contact us today. There are many different tools and strategies for implementation of AI for healthcare providers. We can do a deep dive into your current denial management approach and revenue cycle, formulating a tailored plan to seamlessly incorporate AI into your organization’s revenue cycle management solutions.