Will Artificial Intelligence and Machine Learning Make Denial Management a Thing of the Past?

Time is money, especially when it comes to managing denials. While the typical cost to rework a claim is $25, associated costs substantially increase that amount.[1] An American Academy for Family Physicians study found the cost to correct just 15 denied claims per month can add up to $4,500 per year.[2] With denial rates averaging 5-15% or more, it’s clear this problem can have a significant impact on a practice’s profitability.[3]

AI in Healthcare: A Game Changer for Denial Management

When it comes to advancing denial-management technology, the industry isn’t waiting for the 31% of providers still managing denials manually.[4] According to Health Data Management, “AI technologies like machine learning and natural language processes are on the cusp of making communication between RCM staff and healthcare insurers a 100 percent ‘touchless’ transaction.”[5] A recent report by Accenture concurs, projecting AI will increase workflow efficiencies and improve productivity across all industries by up to 40% by 2035.[6] But healthcare doesn’t have to wait that long; AI is already making a significant impact. Today, AI is used to help providers identify and mitigate denials before they happen.

How AI and ML optimize claim submissions and prevent denials

Applying innovative, advanced technology enables providers to increase revenue-cycle efficiency and accuracy exponentially. Here’s how it works:

• Predicting Potential Denials. Machine learning is applied to a practice’s historical remittance data to identify patterns associated with denied claims. Future claims exhibiting these patterns are then flagged to let staff know there’s a potential issue, while being simultaneously logged into the history of the claim along with any associated rejections. Similar to flashing yellow lights along a highway, these flags alert practice staff to proceed with caution. Note: AI doesn’t take the control away from the provider; staff can still choose to go ahead and submit the claim, but also has the option of pausing to make adjustments based on the ML alert.

• Enabling Proactive Adjustments. When a potentially problematic claim is flagged, AI conducts an analysis across 14 subcategories to identify the root cause. Staff then use this information to create and apply a ‘smart edit’ to the claim before it’s submitted to the payer.Because AI fits seamlessly within the claim workflow, it doesn’t add additional work. Rather, it helps elevate the effectiveness of current processes and existing staff. And while helping to ensure a claim is error-free on the front end might require a couple of extra minutes, this investment of time pales in comparison to what would be required were the claim to be denied.

• Increasing Clean Claim Submissions. Error-free claims on the front end means fewer denials on the back end. And fewer denials can lead to faster payments, improved cash flow, more revenue, and fewer patient billing errors, not to mention more efficient use of staff’s time.

A new era in revenue cycle management begins

With increased consumerism and rising patient- payment responsibility, practices need all the help they can get with capturing revenue. Every claim that is unnecessarily denied siphons money that could be used to hire new staff, replace outdated technology, make office improvements, increase salaries, or enhance patient satisfaction. Leveraging AI can help practices, DMEs, billing services, labs, and other providers prevent denials—and put a stop to lost revenue.

Revenue Performance Advisor from Change Healthcare employs AI and ML to help providers improve clean claims, prevent denials, and decrease days in AR—all in a single, easy-to-use revenue cycle software solution. Providers can benefit from increased efficiencies, enhanced cash flow, and faster reimbursement.

1. Amber Taufen, MGMA assistant editor. “How to avoid ‘unclean’ claims”. MGMA in Practice Blog. March 28, 2014.
2. Richelle Marting, JD, MHSA, CPC, “The Cure for Claims Denials,” American Association of Family Practices, 2015.
3. Jacqueline DiChiara, “Quantify Denial Rates for Smooth Revenue Cycle Management,” Revenue Intelligence, March 30, 3015.
4. Jacqueline LaPointe, “31% of Providers Still Use Manual Claims Denial Management,” July 5, 2016, Revenue Intelligence.
5. “Four technology trends to support revenue cycle management,” Health Data Management, January 29, 2018.
6. “Artificial Intelligence is the Future of Growth,” Accenture, September 28, 2016.