The Current State of AI in Healthcare Revenue Cycle Management

Can AI implemented Predictive Analytics boost our denial management programs?

by: Racquel Williams, RHIA, CPT

 

 

Is your financial performance hindered by denied, suspended, pending, or lost claims?

 

Managing the revenue cycle in healthcare is a complicated process that includes multiple stages, from registering patients to collecting payments. One of the most crucial stages is denial management, an ongoing and iterative process that helps healthcare providers pinpoint and resolve why insurance companies reject their claims. To remain competitive, healthcare providers must continually adapt to changes in the healthcare industry. It can be frustrating for patients, doctors, and healthcare professionals regarding insurance denials. However, predictive analysis technology could reduce the risk of rejections. This post will explain how artificial intelligence (AI) through predictive analytics can boost your denial management workflow in revenue cycle management.

 

(Source: UNITE.AI)

What is Predictive Analytics?

Predictive analytics is a type of data analysis that is used in healthcare to predict a future outcome before it takes place. It looks at current and historical data patterns to determine whether any patterns are likely to emerge again. Then it uses statistical algorithms, data mining, predictive modeling, and machine learning techniques to identify the likelihood of future outcomes. Predictive analytics allows healthcare facilities of any size to adjust where they use their resources, improve operational efficiency, reduce risk, make strategies based on facts, and guide their decision-making process. Predictive analysis has been around for over 75 years. However, people are talking about it more than ever, thanks to the rise in AI. Nowadays, the software used to make predictive analysis is more accessible and affordable. As a result, most healthcare providers can now integrate predictive analysis into their systems, reducing the probability of denials.

 

How is Predictive Analytics used in Denial Management?

 

Over the past decade, the financial industry has utilized predictive models to detect fraudulent credit card charges and reduce unauthorized transactions. This same approach can be used to identify potential claim denials before they happen in the healthcare industry. While it may not prevent all denials, predictive analytics can help healthcare systems avoid specific reasons for claim denials.
 
Payers deny claims for several reasons, and predictive analytics can help eliminate the most common causes, such as missing or incorrect information, inaccurate coding, prior authorization requirements, untimely submission of claims, and patient benefits eligibility or gaps. Denials teams often focus rework efforts on higher value claims, but if the likelihood of a successful appeal is small, the return on investment (ROI) will be disappointing. A higher success rate for multiple smaller claims will have a much greater impact on the provider’s bottom line than higher-value claims that never get approved. With the implementation of AI predictive analytics within healthcare systems automatically categorize denials based on their likelihood of being approved, so staff doesn’t lose time reworking low-value claims.
 
The process of managing denials through AI begins with automated checks of the status of claims. Through the implementation of AI, simple errors can be quickly corrected and resubmitted. In the event of more complex errors, AI can transfer the denial to a human for further review while providing detailed information about the patient and the denial, speeding up the rework process. By utilizing AI-powered denials management systems, organizations can effectively manage denials and ensure that all available reimbursements are collected, resulting in increased revenue and reduced accounts receivable days.

 

Conclusion

The role of AI and predictive analytics in healthcare revenue cycle management is significant, offering healthcare providers advantages that include higher efficiency, reduced errors, increased cash, lower unmanageable debt, and better patient satisfaction and outcomes. As the healthcare industry continues to evolve, AI and predictive analytics are the future for managing patient revenue and satisfaction from initial contact to discharge and final payment. Join us in utilizing the advantages of AI and predictive analytics to enhance our revenue management department and reduce denials more efficiently.

 

About the Author 

Racquel Williams, RHIA, CPT, is an Insurance Verification Representative at Cincinnati Children’s Hospital & Medical Center. Racquel has been a HIM professional for 4 years with experiences in Electronic Content Record Management and professional coding. Beyond that she is a certified phlebotomist.

 

Racquel is currently a student at the University of Cincinnati where she will earn her Bachelor’s degree in Health Information Management. She is slated to graduate in August 2023.