How is AI used in Revenue Cycle Management?

Artificial intelligence (AI) is tackling some of the most pressing issues in revenue cycle management, resulting in higher revenue capture and integrity for early adopters.

 

AI is defined as computers that can achieve goals by replicating "intellectual processes characteristic of humans, such as the ability to reason, discern meaning, generalize, or learn from past experience." 

 

Advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (e.g., Siri and Alexa ), self-driving cars (e.g., Tesla), automated decision-making, and competing at the highest level in strategic game systems are all examples of AI applications (such as chess and Go). 

 

In this blog, we will understand the role played by AI in Revenue Cycle Management. But firstly let’s get an idea of what Revenue Cycle Management is. 

 

What is Revenue Cycle Management?

 

Revenue Cycle Management (RCM) is the process of discovering, collecting, and managing revenue from payers based on the services given by the practice. A successful RCM process is critical for a healthcare practice's financial survival and capacity to continue to deliver great treatment to its patients. 

 

The shift toward value-based compensation and more holistic patient care has compelled healthcare providers to reconsider their approach to revenue cycle management. 

 

Poor billing methods can lead to financial losses and jeopardize the capacity to provide quality care. Efforts to strengthen and streamline fundamental operating operations can assist providers in remaining financially viable.

 

Revenue cycle management (RCM) is a system that allows companies to identify, track, collect, and process revenue generated by their services. This system is often used by businesses in the healthcare industry.

 

Also Read | AI in Healthcare

 

How AI can Modernize Your Revenue Cycle Management

 

AI has also made its way into Revenue Cycle Management. It has brought ideas and solutions to process problems, resulting in improved revenue. With over 80% of medical bills containing errors, AI has proven to be a game changer in revenue cycle management.

 

It's no secret that revenue cycle management has gotten more complicated over the years. Increased patient financial responsibility, intricate payer contracts, and the change to value-based payments are just three factors that contribute to RCM's complexity, yet healthcare companies continue to struggle with collections. 

 

In this article, we will be focusing on how artificial intelligence (AI) is reshaping healthcare by reducing obsolete processes and adding new intelligence to the claims lifecycle.

 

Here's how AI may help to modernize revenue cycle management:-

---

Role of AI in Modernizing Revenue Management Cycle

---

 

1. Claim Denials

 

With claims life-cycle AI, you can proactively identify trouble claims. Revenue cycle managers can build a more focused denial management approach that leads to speedier payments if AI technologies can identify denials with the highest possibility of being overturned.

 

After all, claim denials can be costly. According to Becker's Healthcare, hospitals lose more than $260 billion each year due to insurance denials.

 

Denials can be avoided by employing AI with pre-submission capabilities that aid with fixing authorization concerns. AI can also play a role in your denial management approach by assisting teams in determining which denials are more likely to be addressed quickly, allowing you to allocate resources to the claims that are most likely to be reversed.

 

 

2. Capturing Revenue

 

According to a recent survey conducted by the National Association of Healthcare Revenue Integrity (NAHRI), charge collection is the most important supporting function for healthcare revenue integrity programmes. However, most providers struggle with documentation and coding, resulting in lost revenue. 

 

Even though most healthcare businesses have introduced technology to help with medical code selection, human error still exists and has an influence on the bottom line. 

 

It's no surprise that human error continues to be a problem in revenue cycle management. It was recently revealed that the total number of billable codes has already surpassed 70,000.

 

Introducing AI. AI assists in detecting missing charges before claims are filed, allowing for a more comprehensive claim to be filed and paid on time. AI can also be employed in place of time-consuming and difficult-to-maintain rule-based techniques.

 

 

3. Patient Financial Experience

 

Consumers are paying more out-of-pocket expenditures than ever before, and practices can't afford to lose patients in this highly competitive market. A patient's experience does not end when they leave the office. 

 

Confusion or incorrect remarks can adversely harm the relationship between the patient and the clinician or between the patient and the practice.

 

AI is assisting in improving the financial experience of patients by automatically identifying those who qualify for financial aid and giving the price transparency that patients seek. Patients want to know the cost of their visit before they go, and AI is making this possible.

 

Also Read | AI in Manufacturing

 

How is AI helping in optimizing Revenue Cycle Management

 

According to an American Medical Association (AMA) survey of 1,000 practicing physicians, 86 percent of doctors evaluated the burden of prior authorizations as high or extremely high. Almost the same proportion (88 percent) stated that the load has increased during the last five years. Here are some points through which we can observe how AI helps in optimizing revenue cycle management:- 

 

1. AI is not a new concept in healthcare. For years, forward-thinking healthcare providers have used technology to improve care for people suffering from sleep problems, eye illness, cancer, and even COVID-19.

 

2. The technology has created a buzz for clinical care, promising to detect diseases earlier, improve access to care in poor or developing areas, and minimize the burden of EHR use, among other things.

 

However, integrating AI to revenue cycle management could be the most significant breakthrough in healthcare technology.

 

3. "According to one analysis, approximately $470 billion was spent on billing and insurance-related operations." "There's an incredible amount of labor that goes into having a claim billed and then collecting on that claim," - Ross Moore, MBA, general manager of revenue cycle at Olive, a health IT startup that utilizes AI to streamline provider operations.

 

4. AI performs exceptionally well in high-transaction contexts with established rules, such as the healthcare revenue cycle.

 

5. Polaris noted that the revenue cycle comprises a large amount of tagged data, which means that values are assigned to data points to identify certain occurrences, such as why a claim was refused or characteristics of a patient's diagnosis.

 

 AI can accomplish this by mimicking intelligent human behavior with algorithms  that detect patterns and anticipate future actions to generate a beneficial outcome. 

 

This differentiates from other emerging technologies such as machine learning or robotic process automation, which, like AI, can recognise patterns but are more concerned with improving accuracy than with creating beneficial outcomes. These other technologies, too, have no or limited ability to plan actions beyond the current task at hand

 

As a result, artificial intelligence's "intelligence" can effectively address the most pressing revenue cycle management issues, such as prior authorizations, claim status checks, and out-of-pocket cost estimates, all while getting the information that requires human intervention to the right person at the right time.

 

Also Read | AI in Construction

 

Use of AI in Revenue Cycle Management

 

Within the revenue cycle, there are numerous repetitive operations that can be time-consuming, monotonous, and prone to human mistake. Many tedious chores, such as processing denied claims and missing authorizations, can be removed from your team's to-do list thanks to automation, keeping productivity high and resource costs low.

 

Here are four concrete ways AI and automation might assist revenue cycle:-

 

1. Staying on top of Changing Policies

 

Many providers face major issues when payer payment rules change on a regular basis, resulting in delayed reimbursements, denied claims, higher billing costs, and lost income. 

 

Because providers frequently lack a common repository for sharing updates with the appropriate people across their health system, various departments must devote staff time to digesting the same messages and newsletters. It is incredibly inefficient and can result in thousands of dollars in rewritten claims.

 

You can avoid all of this with automated Payer Alters . Payer Alerts provide workers with a handy service that monitors more than 52,000 web pages from more than 725 payers, ensuring that you are always up to date on any changes. Daily email digests keep you up to date on any specialty-specific updates, allowing you to stay one step ahead.

 

 

2. Actionable insights to help optimize your Revenue Cycle

 

Powerful data analytics are required for a high-performing revenue cycle. However, monitoring and synthesizing all of the data that passes through your business might be difficult. Machine learning algorithms can assist you here by providing greater insights into the performance of your revenue process.

 

You can use a business intelligence tool like Analytics to find predictive solutions that enhance productivity and maximize ROI by leveraging numerous datasets. This application consolidates multiple data sources into a single dashboard, allowing you to track and analyze your organization's performance against your most essential KPIs. 

 

Comparisons of industries and trends research will also assist you in identifying chances for more effective billing, predictive reimbursement, and enhanced payer performance.

 

 

3. Better patient matching to reduce claims denials

 

Incorrect patient identification is responsible for one-third of all refused claims. Each year, hospitals spend an average of $1.5 million on this. The most common option is to use an enterprise master patient index (EMPI) to electronically match and identify patients. 

 

EMPIs, on the other hand, are constrained by their reliance on a single data source — their patient rosters. If an error is introduced into the patient roster, it will be transferred down when patient records are compared. Instead, utilizing a platform like the Universal Identity Manager, patient matching can be automated. 

 

This uses a wide range of more reliable data sources, including Experian's demographic and credit data, to assess the likelihood that two records refer to the same person. Each patient is granted a Universal Patient Identifier, which allows their perfectly matched data to be tracked at all times.

 

 

4. More Efficient Claims Verification

 

Based on their experience and data, your claims staff can definitely predict the likelihood of a rejection, but converting those insights into a robust, efficient procedure is difficult.

 

Claim Scrubber, for example, can look at which claims have been refused in the past and why, and use that data to forecast future denials. It flags at-risk claims so you know to double-check them before sending them to the payer.

 

Also Read | AI in Product Development

 

Healthcare workers are inundated with pitches for products that claim to use cutting-edge AI technology to tackle some of the revenue cycle's most pressing issues while maximizing revenue integrity. Outsourcing to AI healthcare firms can fix the majority of the problems and ultimately enhance revenue paybacks.