For early users of artificial intelligence, it is tackling some of the largest pain areas in revenue cycle management, resulting in increased revenue capture and integrity. One of the most time-consuming parts of revenue cycle management is prior authorizations.
The AMA(American Medical Association) conducted a study of almost 1,000 professional physicians and found that 86 percent of doctors rated the burden of prior authorizations as high or extremely high. In addition, almost the same percentage (88%) indicated the load has increased in the past 5 years.
According to the report, physicians and their staff spend over two full business days per week processing prior authorizations, and more than one-third of physicians have personnel dedicated only to the task.
Prior authorization is now one of the most promising artificial intelligence applications in healthcare. Prior authorizations are a great target for AI technology because of their transactional nature. AI can employ real-time analytics and machine learning to identify cases that require prior authorization, send requests to payers, and track statuses.
It’s a transaction-oriented company in that every patient in need of medical care will have a considerable number of transactions, from scheduling through the several steps required to construct a clean claim, submit it, and get reimbursed.
Artificial intelligence (AI) is not an unknown 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.
The technology has sparked interest in clinical care, with promises of speedier illness detection, expanded access to care in underprivileged or emerging areas, reduced EHR use burden, and more.
However, integrating AI into revenue cycle management could be the biggest break in healthcare for the technology.
The healthcare revenue cycle, for example, is a high-transaction setting with established rules that AI excels at.
The revenue cycle has a lot of tagged data, which implies that values are assigned to data points to signify particular occurrences, such as why a claim was refused or the diagnostic characteristics.
AI can use algorithms to imitate intelligent human behavior and plan future actions to achieve a favorable end. This is in contrast to other emerging technologies like machine learning and robotic process automation, which, like AI, may spot patterns but are more concerned with improving accuracy than delivering good results. These other technologies have limited or no ability to plan actions beyond the current task.
As a result, AI’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, while simultaneously getting the information that requires human intervention to the right person at the right time.
Providers are inundated with offers for technologies that claim to use cutting-edge AI to solve some of the revenue cycle’s most vexing problems while maximizing revenue capture and integrity.
Despite the apparent increase in product offerings and the revenue cycle’s readiness for automation, AI adoption for revenue cycle management is not as widespread as one might imagine.
Providers are preparing for a larger-scale adoption. According to a recent poll of C-suite executives, less than half of healthcare businesses (44%) are now using AI in some form or another, but 88 percent expect widespread adoption within the next five years. According to respondents, these implementations would influence revenue integrity, clinical documentation improvement, coding, and other aspects of the revenue cycle.
However, getting everyone on board with spanning the chasm could be difficult.
Whether one accepts the hype, AI is establishing a foothold in the healthcare revenue cycle. Although the market for AI in revenue cycle management is still being worked out in healthcare, providers can still reap the benefits of the cutting-edge technology. Identifying use cases is an excellent place to start with AI investments. First, providers should be on the lookout for ways to automate processes that have a negative impact on net revenue. Then they should figure out how much it will cost to hire people. Stakeholders should inquire about the number of individuals executing a certain role. Where can automation free up capacity for higher-value activities?
