How AI can hack away at administrative waste
There is a mountain of administrative waste in healthcare. Hacking away at that waste could save mountains of cash and improve hospital and health system margins.
There are IT tools that can help provider organizations reduce administrative waste, tools such as artificial intelligence, machine learning, revenue cycle data analytics and others. Knowing how to deploy and use these tools is the key.
Which is why we sat down with Brian Robertson, CEO of VisiQuate, a vendor of advanced revenue cycle analytics, intelligent workflow and AI-powered automation. Here, Robertson discusses what administrative waste looks like, how AI and machine learning can be used to combat revenue cycle anomalies, how strategic automation and revenue cycle data analytics can diminish waste, and how healthcare data, analytics and AI chatbots can help administrative staff get provider organizations on more stable financial ground.
Q. You cite nearly $1 trillion in administrative waste in healthcare. What does this look like? Why is this happening?
A. At the most fundamental level, it is the sheer complexity of our U.S. healthcare system of nearly 1,000 distinct payers and what is still largely a fee-for-service reimbursement system, where on average 40% of claims are not paid electronically on the first pass, and in many cases are still often worked for resolution on a one-by-one basis.
Other countries that operate a single payer system have inherently much more standardization, particularly as it relates to administrative costs. In the U.S. healthcare market, the average hospital or health system has multiple systems of record, including multiple peripheral systems, bolt-on applications and integration of third-party data sets.
These market realities result in significant data fragmentation and operational friction as complex data sets are not often cleansed, normalized and curated to enable highly efficient operational workflow. The overall waste or excess overhead or spending is a combination of administrative, operational, and clinical support systems and functions, and each is wrought with tremendous duplication, workflow redundancy, and other low-value or process waste inefficiencies.
All in, most studies suggest that an average of 25% and growing of every U.S. healthcare dollar, in a $4 trillion dollar industry, exists in some form of industry waste. And according to most studies, the largest and perhaps easiest area to gain some real traction in optimizing value is in the areas of non-clinical waste.
In addition to inherent industry fundamentals, the conventional wisdom is that U.S. healthcare is a decade or longer behind in overall technology adoption, including leveraging modern, high-value technologies such as AI and machine learning.
The current adoption of AI and machine learning in the RCM arena is still nascent and in the early adopter phase of the technology adoption lifecycle. And while there’s been a lot accomplished as it relates to purely digitizing information assets, enabling data for action and leveraging more advanced technologies such as cognitive or intelligent process automation to reduce process is still lagging.
Q. How can artificial intelligence and machine learning be used to battle revenue cycle anomalies that erode hospital margins?
A. Many hospitals and health systems have begun their journey, with various industry surveys suggesting two-thirds have begun to invest in and implement some form of AI. Robotic process automation appears to have gained the most traction up to this point, including task automation in key areas such as eligibility, pre-authorization, and patient account follow-up and collections management.
In addition to robotic process automation, there are additional AI subsets such as machine learning, predictive analytics, natural language processing and cognitive process automation.
