Skip to main content

Artificial Intelligence (AI) is Changing the Revenue Cycle Management Processes

January 25, 2021

AI in Revenue Cycle Management ProcessesNavigating a global pandemic led to many changes in healthcare, both on the practice side and when it comes to behind-the-scenes operations. As we move forward into 2021, practices and healthcare systems are considering how to adapt and recover in the coming months, making the topic of artificial intelligence (AI) in healthcare and revenue cycle management a hot topic.

Automation and AI have the potential to transform the way we think of revenue cycle management, and healthcare leaders continue to believe that AI is a high priority. Surveys show that 75% of healthcare leaders are currently implementing or plan to implement an AI strategy, with 43% saying their first step will be automating business processes like revenue cycle management.

AI in healthcare and revenue cycle management has progressed far beyond being just a buzzword, and today, it’s being used actively to improve cycle revenue outcomes and efficiency. But how can artificial intelligence modernize and improve revenue cycle management? Here’s how.

Capturing More Revenue

While a National Association of Healthcare Revenue Integrity shows that the top supporting function for revenue integrity programs in the healthcare industry is charge capture, providers continue to struggle with coding and documentation challenges that lead to lost revenue. Although practices implement technology that assists with the selection of medical codes, human error continues, impacting a practice’s bottom line significantly. It’s reported that there are more than 70,000 billable codes, making it easy to see why human error continues to be a problem in revenue cycle management.

Here’s where artificial intelligence can help. With AI, it’s possible to detect coding problems or missed charges before filing claims, ensuring a more complete claim is filed and paid in a timely manner. It’s even possible to use it instead of rules-based methods that can be difficult to maintain and time consuming.

Reducing Claims Denials

Becker’s Healthcare estimates that insurance denials cost hospitals over $260 billion each year. Although over half of denials can be recovered, handling denials costs over $100 per claim in administrative costs to challenge a claim and receive the monies owed.

Machine learning allows practices to predict which claims could be denied before they’re even submitted. This is done by identifying common root cases of denials by CPT code and payer, applying this information when automatically reviewing claims, flagging areas of a claim that may have incorrect or missing information, and alerting staff to follow up.

With AI, staff have the chance to correct problematic claims before submission, reducing claims denial rates. This also lets revenue cycle teams manage their work relating to denials more effectively, helping them focus on and allocate resources to the highest-value denials and those that have a significant change of being overturned.  

Improving the Patient Financial Experience

Statistics show that patients today pay more out of-pocket costs than they ever have before. This means that the patient experience is critical, and practices can’t afford to lose their patients. This means it’s not enough to provide a great clinical experience; you must also provide patients with an excellent financial experience. Inaccurate or confusing billing statements damage relationships between practices and patients.

Once again, AI can help. AI has the potential to automatically identify patients who may qualify for financial assistance, and it also helps offer price transparency – a key thing patients desire. Patients prefer to know what their visit will cost before an appointment, and AI helps make that possible. AI may even be used to help revenue cycle teams come up with targeted collection strategies based upon a patient’s preferences and previous payment behavior, resulting in customized billing approaches that increase the likelihood of collection.  

Automating Highly Manual Tasks

How much time and money gets spent on highly manual revenue cycle management tasks? AI gives providers the ability to replace highly manual, time-intensive, error-prone tasks with automated processes, which allows staff to direct their efforts towards tasks that offer the greatest value.

Many front-end revenue cycle processes are primed and ready for automation. One example is the ability to automate patient eligibility checks in real-time, ensuring providers have the latest information on a patient’s coverage and the portion of their deductible they’ve met to date. Pulling benefit information automatically reduces manual data entry, lowering the risk of error. Since studies show that eligibility and registration errors account for nearly 24% of errors, the potential for increased revenue is huge.

As you move towards the future of revenue cycle management, can help. We are building our own AI tools to help practices with medical billing and coding, and more, ensuring you have time to focus on what your practice does best. Contact today to discover how we can help you.


Get the Latest RCM News Delivered

Receive practical tips on medical billing and breaking news on RCM in your inbox.

Get in Touch