Are you aware that 97% of the healthcare industry trust AI in revenue cycle management to handle
their billing and clinical works? Fine! I can’t disagree that 85% are still implementing or
developing some AI strategy. As we all know, AI in revenue cycle management companies has been
evolving rapidly and already surpassing human decision-making in certain areas. There are areas
where it can’t be utilized. At the same time, we can’t deny the compelling and dramatic results AI
has rendered to the business today.
There were times where human decision-making spoke the most of the business profits and eventually
slowed down the ranking due to the inability to perform with constant accuracy. NY Times says that
healthcare professionals have been impacted the most by AI in revenue cycle management. AI is
ideally suited to answer some of RCM’s most significant barriers when it comes to the revenue cycle,
and it can be implemented right away.
This article provides you with enough information on those barriers that are stalling AI in revenue
cycle management and how it improves the revenue cycle in the healthcare industry. Let’s drive right
away!
3 Barriers AI in Revenue Cycle Management needs to overcome:
AI in revenue cycle management can be defined in various ways. But, ultimately, the ability of the
machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning,
learning, interacting with the environment, problem-solving, and applying creativity, matters a lot.
It includes the subsets of AI like machine learning (ML), predictive analytics, robotic process
automation (RPA), natural language processing (NLP), and optical character recognition (OCR).
AI in the revenue cycle is not a new subject. It’s just the adoption became slow compared to the
other industries.
Recently, several healthcare professionals are choosing AI as their crucial source to fulfill their
administrative and clinical tasks.
A recent report says that most hospitals expect to be using AI within three years for revenue cycle
management.
However, many of them have to adapt to
AI technology and
overcome the barriers. The application has become very limited for the professionals who are already
used to AI in revenue cycle management and doesn’t span the revenue cycle from end to end.
With the variety of conflicting viewpoints, three barriers could stall the growth of the business.
They are:
Budget and cost concerns
Privacy and security concerns
Personnel concerns
Budget and Cost concerns of AI in Revenue Cycle Management:
A recent study asked its respondents to share the reason why their organization didn’t come forward
in investing for AI in revenue cycle management?
Almost 60% of respondents revealed the cost concerns in AI and whether it will deliver services
right on time.
C-suite and financial leaders are especially concerned about budget constraints, whereas 6% revealed
that they don’t require technology at this point.
This apparently shows a significant gap between the need for the solutions and organizations’
ability to afford them.
For the organizations to require and realize greater adoption, they need a clear ROI, which helps
overcome their doubts.
The more outstanding issue with most of the revenue cycle processes is that it results in friction
and waste.
The amount of work that goes into managing the revenue cycle is very complex, business with
transaction-oriented, and every patient has a significant number of transactions from scheduling
appointments to the multiple steps to create a claim, submit it, and receive reimbursements.
AI in revenue cycle management could be technology’s most significant achievement in healthcare.
Manual and redundant tasks that occur during patient access, coding, billing, collections, and
denials can be automated with the help of AI.
In fact, AI in revenue cycle management can handle high transaction environments with coding rules,
like the healthcare revenue cycle.
Of course, AI already has been addressing some of the most challenging issues in RCM related to cost
concerns, leading to increased revenue capture.
By automating few data-driven tasks, administrative waste can be automatically and dramatically
reduced, and RCM operations can move more efficiently and effectively.
Privacy and Security Concerns:
One of the other top concerns of the healthcare industry about AI in revenue cycle management is
that they don’t trust the accuracy of the results.
Nearly 56% of them accept that their organization is gradually becoming slow in adopting AI
technologies to avoid emerging risks.
Simultaneously, the same amount of people acknowledge that negative public perceptions will also
slow or halt the adoption of some AI technologies.
Experienced adopters of AI believe that they will have to improve the way they handle emergency
risks.
Few key steps to avoid emergency risks in AI are:
Keeping track of your AI models, algorithms, and systems through a formal inventory.
Actively addressing the risks by creating your own ethics policies or adopting one that is broadly
supported.
If a healthcare organization is encouraging the approach of collaborating on AI ethics, it means
that it has created a new role to lead AI to work with the chief risk officer on AI governance.
There will also be challenges that arise as healthcare organizations adopt AI and transform their
business, coordinating and being transparent with teams and focusing on a cost-effective,
patient-centered approach.
It will encourage the organization to move forward and go for the actual change with AI strategy.
Personnel Concerns:
From the view of specific organizations or companies, AI is another burden to hire, train, as well
as retain new personnel for another new system.
A few of their concerns include adding more workload. Still, AI assistance actually can change the
way people work with technology and help improve work-life balance, relaxing the employees up to do
more meaningful work.
A recent survey revealed that people who use digital workers, RPA, and software robots are estimated
to save an average of 26 hours per week.
Comparatively, working with those digital workers, 34% of them stated that AI was most helpful in
sorting and classifying data and documents.
AI in revenue cycle management can make compliance easier and monitor clinical operations by
providing office staff visibility to improve the process and boost productivity.
In fact, investing in AI is risky and demanding for the organization to manage initially. After
results will make the challenging barriers fade out and increase the revenue growth. Due to this
pandemic, most of the revenue leaders are working remotely, and AI in revenue cycle management is
definitely a go-to option!
Here is a recent survey report conducted among Healthcare Professionals. You can
download the report here!
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