RPA's enormous potential in the future of revenue cycle

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There’s a lot of buzz about the potential of robotic process automation (RPA) in this age of cost reduction and staff shortages. In fact, the global market size of this one area of automation technology is expected to top $1.8 billion in the healthcare industry alone by 2028.

RPA is making waves across many industries. Specifically in healthcare, these tools have been highlighted for their potential to impact a wide variety of use cases, from improving customer service and supply chain management to addressing inefficiencies in accounting, human resources and revenue cycle.

Especially at a time when practices are reeling from severe billing department staffing shortages and tight operational margins, RPA could emerge as one targeted solution that could help financial executives maintain a healthy bottom line. For instance, automating key areas of revenue cycle such as scheduling, payment posting and claim status checks could reduce the need for human intervention in those specific processes.

However, deployment of RPA technologies without thoughtful planning, analysis and data-defined targeted outcome could set a business up for failure. Before implementing RPA, effective strategies begin with a fundamental understanding of practice’s current processes and a specific result in mind—especially where humans are getting involved and where breakdowns are occurring.

RPA and revenue cycle: The Need for Better Intelligence

The goal of RPA is to meaningfully carve away work from humans. The reality is that while automation can streamline revenue cycle workflows, it will never fully remove the need for human intervention. But, when placed strategically into processes, it can enable tasks to be completed in a fraction of the time with consistent accuracy, allowing executives to maximize the skill sets and expertise of scarce human resources.

The best approach leverages RPA for repetitive, rule-based and transactional type tasks. Consequently, before investing in RPA technology, you must understand how staff members are currently spending their time and where inserting RPA makes the most sense. To conduct this type of deep analysis, healthcare organizations must have a method for documenting every part of a process workflow. Even more importantly, financial leaders need clear, structured data-driven visibility into where current processes may be breaking down and how much those broken processes are costing the organization in time and money. There is no “one size fits all” solution from an RPA standpoint. Every organization faces unique obstacles and will require a tailored roadmap that describes which automation will be introduced and when.

Consider that a medical enterprise may identify a state-of-the-art RPA tool for payment posting to automate data entry processes. While on the surface, this may seem a solid investment, an analysis of operational data may confirm that it is not the best use of limited dollars and may not be the root cause of cost of overruns. If the current process is bad or not optimized, automating an already sub-optimal process gets you nowhere fast. Maybe the real opportunity is not posting efficiencies but rather that your practice management system does not auto-post electronic remittance advices (ERAs), or you aren’t set up for ERAs to begin with. Your financial or practice management system data should show you both your configurations around ERAs as well as user level production and effectiveness in posting processes.

With total visibility into the revenue cycle process and the activity of staff members, many executives are likely to find that perceived problems weren’t real problems—rather, they were high value bottlenecks existed which cost them material amounts of money each month.

Laying the right foundation for RPA with effective intelligence

To effectively lean into RPA as a strategy to address ongoing staff shortages, healthcare executives must become more “effective” in their use of revenue cycle resources. This requires that they derive deeper insights from structured data to achieve a model that ensures staff are effective, not just productive, at their work and generating the expected results.

While many provider organizations rely on EHRs and practice management systems for basic analytics, these infrastructures are simply not equipped to provide the level of visibility needed for effective intelligence. One cost-efficient way to achieve the needed level of data granularity is to invest in a centralized data warehouse that brings together data from multiple sources—practice management systems, EHRs and workflow automation solutions.

More specifically, robust analytics targeting staff processes, productivity and outcomes allow providers to dig deeper into staff workflows and retrieve insights that provide the best information for decisions about how to insert RPA and automation into key touchpoints of the revenue cycle. Some key questions that can provide insight into where breakdowns are occurring include:

• What are my AR staff doing with each touch on a claim and could any of this be automated via RPA?
• What level of effort is required (# of hours, full-time equivalents) for the task being considered for RPA implementation?
• What metrics have been established to ensure proper oversight and monitoring?

It’s true, RPA can carve work away from staff, but first you must have the intelligence in place to understand where breakdowns in processes are occurring to know what to automate. When robotics are involved, there is always a need for a system of checks and balances, so identifying the best areas to implement these automation tools is key to long-term ROI and success. Effective intelligence provides the foundational knowledge needed to capitalize on emerging technologies and reduce human intervention in the revenue cycle in the most impactful way.

With the right visibility into staff effort, RPA could certainly be strategically inserted into workflows to ensure staff work smarter. For instance, key areas of consideration for RPA could include:

• Manual data entry: data which is stored across different systems can be aggregated to enter through a central repository (i.e., electronic claim submissions)
• Account status checks: checking the status of outstanding insurance claims and integrating that into a collector’s exception-based workflow so he/she never has to touch those which are either still in progress or waiting to be paid
  • conversely, accounts which are denied can immediately be routed to someone’s worklist for intervention
• Additional examples: securing prior authorizations, logging into enterprise-wide apps, moving files and folders

Other considerations/questions that can help healthcare organizations approach RPA optimally include:

• Is the process prescribed and can it be described through “if then” rules without human intervention?
• Can the bot retrieve data which is accurate and reliable?
• Is the organization going to go through any major system changes or upgrades in the next 6-9 months? Scripts will most likely have to be re-written when that happens.
• Are there controls in place to ensure any changes in process or system are up to date? (Otherwise, this will bring the bots down)

As financial leaders consider how best to leverage RPA, they should do their due diligence by analyzing current processes to accurately determine desired outcomes. Having tools that support the right foundation of intelligence will provide additional views and ideas around RPA. When paired with effective intelligence, RPA can be optimally inserted to redirect resources (which were previously tied up performing manual, repetitive, non-value-added tasks) to more mission critical areas.