Did you know, a survey revealed that out of 1,000 practicing healthcare professionals conducted by the American Medical Association (AMA), 86 percent of physicians explained the burden of prior authorizations as extremely high? AI in revenue cycle management has been addressing some of the vital pain points leading to increased revenue capture and integrity for early adopters of the technology. Prior authorizations have become one of the problematic aspects of revenue cycle management.
Nearly 88% of healthcare professionals also revealed that the burden has increased over the last five years. Adoption of AI in revenue cycle management and electronic prior authorizations increased by just 1 to 13 percentage from 2018 to 2019. CAQH reported using data from medical plans covering nearly half of the US’ insured population. This article reveals a clear picture of how AI in revenue cycle management improves revenue integrity besides all the chaos.
AI in revenue cycle management works on Prior Authorizations too!
- Sharlene Seidman, a vice president at Yale-New Haven Health, recently expressed that “It’s very, very resource-intensive on both sides, not just on the provider side. It’s a series of interactions that are very transactional”.
- Such interactions add to the conversation, and healthcare professionals, including their staff, had to spend almost two full business days each week completing prior authorizations. More than one in three physicians have staffs who work exclusively on the task, the American Medical Association survey found.
- “The transactional nature of prior authorizations makes it ripe for automation,” Seidman stated. But the electronic way of adoption of prior authorizations remains low, according to data from the Council for Affordable Quality Healthcare.
- “Numerous barriers have prevented or slowed the adoption of electronic prior authorization,” revealed CAQH. These barriers also include a lack of operating rules and standards, infrastructure, and vendors being competitive.
- “There is a series of repetitive tasks that our staff has to follow, and the automation solution that we’re seeking with Olive can learn those steps and replicate them, and in the future even anticipate if there are changes based on historical denials that we’ve received,” Seidman explained.
- “We have seen some early successes with cash coming in a little more quickly, claims getting resolved more quickly. It’s working,” she stated.
- Prior authorization is considered one of the best cases for AI in revenue cycle management as of now. The transactional nature of prior authorizations makes it an ideal candidate for AI in revenue cycle management. It can also leverage real-time analytics and machine learning to identify cases needing prior authorization, submit payer requests, and check statuses.
- But prior authorizations are also considered one of the burdensome processes in a transaction-heavy part of healthcare.
- “The level of complexity in managing revenue cycle is very high,” explained Joe Polaris, SVP of product and technology at R1 RCM, a revenue cycle management vendor.
- “It’s a very transaction-oriented business in that every patient who has a need for medical care is going to have a significant number of transactions from the point of scheduling all the way through the multitude of steps to create a clean claim, submit it, and get paid.”
- It makes it very difficult to scale, especially for large-sized organizations. The only way to achieve scale without the right technology is “to hire really smart people, and make them work even harder, and get even smarter.”
AI in RCM has the potential to Optimize
- AI in revenue cycle management is not a new subject. For many years, innovative healthcare providers have been leveraging AI technology to deliver better patient care for sleeping disorders, eye disease, cancer, and Covid-19.
- But applying AI to revenue cycle management could be the technology’s most significant break in healthcare.
- “There was a study done that estimated about $470 billion was spent on billing and insurance-related activities. The reason for that was there’s an obscene amount of work that goes into getting a claim billed and then collecting on that claim,” said Ross Moore, MBA, general manager of revenue cycle at Olive, a health IT company that uses AI to automate provider workflows.
- “Those manual, redundant tasks that are taking place in patient access, coding, billing, collections, and denials, those tasks themselves that are performed by the revenue cycle departments can actually be automated using AI.”
- “Whether it’s matching a patient with the right provider, estimating out-of-pocket costs, or coding the claim, those are things that have long lists of variables associated with them, and AI is pretty uniquely good at evaluating those variables and coming up with an ever-improving success rate of getting to the right outcome against any of those process steps,” he said.
- AI can do it by imitating intelligent human behavior through algorithms that find patterns and plan future actions to produce a positive outcome.
- Therefore, 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, all while getting the information that needs human intervention to the right person at the right time.
- “When the technology is used inside of Epic, Cerner, or our standalone clinical intelligence products, it looks based on what it learns in the document, and then it renders the information to the appropriate party,” stated Michael Clark, senior vice president, and general manager for provider solutions within Nuance’s healthcare division.
- “So, there’s a case manager who is interested in what’s going on in encounter documentation. There’s a coder. There’s certainly a physician. Through AI and machine learning, you can make available, out of the same set of facts and circumstances in the data, information to those different stakeholders, eliminating redundancy, reworking, and retrospective queries.”
- “Providers need to know in real-time who is cleared to be treated financially and who is not to make the most informed decisions for the practice. Patients, too, need to know what they’re responsible for paying so they can choose treatment options that are right for them,” said MaryAnne Thompson, controller at MidLantic Urology LLC, a group of 70-plus physicians in Pennsylvania.
Healthcare professionals are bombarded with the pitches for products that promise to leverage the latest AI technology to solve some of the revenue cycle’s crucial pain points while maximizing revenue integrity. Outsourcing to AI healthcare companies can solve most of the issues and increase revenue paybacks ultimately.
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