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3 Barriers Stalling AI Adoption in Revenue Cycle Management

3 Barriers to AI Adoption in Revenue Cycle Management
Kenya Smith, Healthcare Solution Marketing Manager at ABBYY

AI technology is quickly evolving and already surpassing human decision-making in certain instances; sometimes, in ways, we can’t explain. While many are alarmed by this, AI is producing some of the most effective and dramatic results in business today. 

Adoption of AI in healthcare is growing, but there are still barriers to overcome

AI can be defined in many ways but, broadly, it is the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and applying creativity. This includes subsets of AI such as machine learning (ML), predictive analytics, robotic process automation (RPA), natural language processing (NLP), and optical character recognition (OCR).

AI is not new to healthcare, but the market has traditionally fallen behind compared to other industries when it comes to adopting and using artificial intelligence (AI). Now, more and more healthcare leaders are turning to AI. In fact, a recent survey reports that nearly all hospitals expect to be using AI within three years for revenue cycle management (RCM). However, many have yet to adopt the technology due to how they perceive barriers related to it, and for those already using AI in RCM, its application is limited and doesn’t usually span the revenue cycle from end to end.  

While there is a variation of conflicting viewpoints that healthcare leaders have around AI and the barriers to RCM, there are three areas that could stall progress:

1. Budget and cost concerns

A Change Healthcare research study recently asked respondents to share why their organization has not invested in AI for revenue cycle management. Nearly 60% are concerned about the costs of AI and whether it will deliver timely ROI. C-suite and financial leaders are especially concerned about budget constraints (76%). Only 6% say they don’t need the technology, which shows a significant gap between the need for the solutions and organizations’ ability to afford them. In order for the organizations to realize greater adoption, a clear ROI is necessary to overcome doubts.

The issue with many revenue cycle processes is that they can result in a lot of friction and waste. There is an obscene amount of work that goes into managing revenue cycle; it’s a complex, transaction-oriented business as every patient has a significant number of transactions from the point of scheduling appointments to the multitude of steps to create a claim, submit it, and receive payment. Applying AI to RCM could be the technology’s biggest break in healthcare. Those manual, redundant tasks taking place in patient access, coding, billing, collections, and denials that are performed by revenue cycle departments could be automated using AI. AI actually handles high transaction environments where there are codified rules, like the healthcare revenue cycle.

AI is already addressing some of the biggest pain points in RCM related to cost concerns, leading to increased revenue capture. When a healthcare provider from Pennsylvania implemented AI technologies into their emergency department, they found $1.2 million in additional revenue from a decrease in LWBS (left without being seen), $123,000 in annual cost savings from eliminating CT over-ordering, and $96,000 in annual cost savings from reducing labs ordered with no results. AI was able to provide a clear picture of how processes execute on a daily basis, helping them target their efforts to eliminate inefficiencies in the areas where they’ll be most effective.

By automating certain data-driven tasks, administrative waste can be dramatically reduced, and RCM operations can move more efficiently.

2. Privacy and security concerns

Another top concern from healthcare organizations is they don’t trust the accuracy of the results. In fact, 56% agree that their organization is slowing the adoption of AI technologies because of the emerging risks, and the same proportion believes that negative public perceptions will slow or stop the adoption of some AI technologies, according to a Deloitte study.

Even more, experienced AI adopters have room to improve how they manage AI-related risks. A couple of key steps for avoiding risks include keeping track of your AI models, algorithms, and systems through a formal inventory, and actively addressing the risks by creating your own ethics policies or adopting one that is broadly supported. For example, one organization is tackling AI-related risks by embracing the approach of collaborating on AI ethics. It created a new role to Lead AI Governance for the firm to work with the chief risk officer on AI governance.

While there will be challenges that arise as organizations adopt AI and transform their business, coordinating and being transparent with teams and focusing on a cost-effective, patient-centered approach will help the organization move forward and drive real change with its AI strategy. 

3. Personnel concerns

From some companies’ perspectives, it’s just one more thing on their list to have to hire, train, and retain new personnel for another system. Some view that adding another system may add more to their staff’s workload, but AI assistance can change how people work with technology in a positive way and can actually help improve work-life balance, freeing workers up to do more meaningful work. A recent survey found that those who use digital workers – robotic process automation (RPA) software robots – estimate that they save them an average of 26 hours a week in productivity. Of those working with digital co-workers, 34% said they were most helpful at sorting and classifying data and documents. Those who wished they had a digital co-worker said they would use them for digitizing paper, prompts, and classification purposes and they could save 54 days per year using a digital colleague.

AI technologies can make compliance easier by monitoring clinical operations in real-time while providing your staff with visibility into where improvements can be made or where a revenue cycle process broke down, and alert them when protocols are not followed or process misbehaviors are detected. With the pandemic forcing revenue cycle teams to work remotely, leaders are finding it harder to find the root cause of process issues and fix them. Additionally, AI solutions like these typically do not require any technical coding skills, making it easier for more staff to use and understand, and help them simplify some of the complexities that exist in their role.

Benefits of AI in RCM

AI technologies have helped healthcare organizations to streamline and provide more accuracy to healthcare revenue cycle management strategies, as well as manage large volumes of information and inform employees of revenue cycle management goals, especially through the use of dashboards and alerts. Many providers have benefitted from automating common issues with healthcare revenue cycle management, such as payer-improving payer-provider communications, recommending appropriate ICD-10 codes, monitoring medical billing processes, and even scheduling patient appointments. 

According to an Accenture analysis, health AI presents opportunities across a diverse set of areas, with the top 10 AI applications including robot-assisted surgery; virtual nursing assistants; administrative workflow assistance; fraud detection; dosage error reduction; connected machines; clinical trial participant identifier; preliminary diagnosis; automated image diagnosis; and cybersecurity – with a potential total financial value of about $150 billion. As AI applications, including these top 10, gain experience in the field, their ability to learn will continually lead to improvements in precision, efficiency, and outcomes.

AI can also help remove administrative waste due to inefficient revenue cycle practices, enhance decision-making, and improve patient engagement. For example, an AI platform can streamline patient access, optimize the claims lifecycle, guide capacity planning, and more. The opportunities are endless.

Investing in AI

Figuring out the path to AI implementation is one of the toughest challenges an organization may face in its journey. But the work done during the early adopter stage will be key to AI investments in the future. A good place to start with AI investments is identifying use cases. What are the key problems you’re trying to solve and why are they hard to address? What are the operational consequences if you don’t solve them? 

Once you decide on what processes to automate, you can then select a technology partner. Look for a company that you share the same goals with. For example, if your goal is to have the patient in the center of everything being done, go with a vendor whose mission is not to replace staff with AI, but to redirect staff to doing more patient-centric activities. Organizations should go with a partner who is an expert in healthcare with varying degrees of success, rather than a partner that does everything. Additionally, look for partners who know AI and know how to apply it to your workflows and processes not just to automate or partly automate what’s there, but in many ways to reinvent them.

RCM is, and should, continue to evolve and keep pace with rapid changes to the healthcare ecosystem. Healthcare professionals should always be aware of how their revenue cycle is doing and must expand their use of AI in order to provide appropriate care and the best service for their patients while receiving correct reimbursement, and remaining competitive.  


About Kenya Smith

Kenya Smith is the Healthcare Solution Marketing Manager at digital intelligence company ABBYY. Kenya brings over 16 years of experience in R&D, consulting, training, and support – all within the healthcare industry. 


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