The role of intelligent automation in reducing waste and improving efficiency in the revenue cycle
A recently published study in JAMA and reported in the New York Times found that nearly a quarter of annual healthcare spending is wasteful, with the largest source being administrative expense, totaling $266 billion per year. Although the definition of waste certainly includes unnecessary activity and rework, a broader view of the term includes inefficient tasks and processes that can be made more efficient or even eliminated using modern technology. One such technology that is disrupting traditional methodologies in the revenue cycle today is intelligent automation.
A recently published study in JAMA and reported in the New York Times found that nearly a quarter of annual healthcare spending is wasteful, with the largest source being administrative expense, totaling $266 billion per year. [a]
Although the definition of waste certainly includes unnecessary activity and rework, a broader view of the term includes inefficient tasks and processes that can be made more efficient or even eliminated using modern technology. One such technology that is disrupting traditional methodologies in the revenue cycle today is intelligent automation.
Intelligent automation technologies including robotic process automation (RPA), machine learning and artificial intelligence offer a way to reduce revenue cycle expense and drive down the overall cost to collect, a critical metric of any revenue cycle.
The goal behind applying intelligent automation in many cases is to improve the efficacy of problem diagnosis and root cause analysis and to then minimize the often routine and rote processing tasks, allowing staff to focus on exceptions. This prioritization allows resources to be allocated to tasks that require additional judgement, which may involve analyzing complex and unstructured data to reach a decision on what actions to take next.
If staff can be focused on exception processing, and the automation is directed to process the bulk transactions that can be distilled to well-defined business rules and structured-information flows, then significant improvements in employee morale and cash collections will be the result. RPA is particularly well suited to off-loading those tasks that are high volume and low complexity, with relatively little variation from one transaction to another.
While automation technology can assist across many aspects of this problem, there are specific business processes in the revenue cycle that are better suited to one type of intelligent automation than another.
Best use of intelligent automation
Let’s look at a few of the major functions within the revenue cycle to see where intelligent automation can be best applied to improve efficiency and reduce wasteful spending, according to the 2018 CAQH Index.[b]
Eligibility and benefit verification. Electronic transactions for eligibility and benefit verification have increased by more than 9 percentage points over the last three years. This is a positive development, but more can be done to reduce wasteful spending in this part of the revenue cycle. As the starting point for care delivery, this function represents a significant potential for improvement via intelligent automation.
One aspect of this function that is ripe for disruption through RPA is the enormous volume of phone calls between providers and health plans. Annual call volumes are estimated at over 1.46 billion, representing a savings opportunity of more than $4 billion per year, according to the 2018 CAQH Index. RPA offers several approaches to reducing not just call volume, but also the time spent on hold by provider representatives and their partners. Many healthcare organizations report in excess of two hours per day spent of the phones with most of that time taken up by waiting on hold and navigating interactive voice response (IVR) systems. RPA technology offers a way to eliminate the hold time and IVR navigation, thus freeing up resources to work on higher value activities, boosting both productivity and employee satisfaction.
Prior authorization. The tasks necessary to clarify, request and obtain approval for coverage for specific services are also ripe for application of intelligent automation. Many health plans use web portals for this process, requiring providers to navigate a diverse and inconsistent set of data requirements for each payer. Although adoption of electronic data exchange for this process is generally low across providers (12%), there is cause for optimism that RPA will allow broader adoption in the future.
Errors in the prior authorization phase of the revenue cycle may account for nearly 24% of all claim denials, which makes this area a high priority for application of RPA.[c] One of the primary features of an RPA bot is that it performs the same tasks repetitively each time without variation, essentially reducing the error rates of formerly human-driven transactions to zero.
With manual prior authorizations requiring an average of 16 minutes and as much as 30 minutes per transaction, the opportunity to drive cost savings through application of RPA is significant. The effort involved in this process is a major cost driver. With transactions costing as much as $6.61 each, RPA can reduce costs to just a few cents each.
Credit balance adjustment. One of the most time-consuming and labor-intensive functions within the revenue cycle involves application of credit balances and the adjustments to patient accounts that must be made in a timely fashion.[d] In this critical process, inefficiency can create a compliance risk. RPA can help reduce this risk as a secondary benefit to improving productivity. Bots can be configured to identify the same payments from the same health plan and then process the same transaction, crediting the appropriate patient account each time. Reversing credit balances using bots instead of manual processing can save a significant amount of staff time.
Claim status update. Adoption of electronic claim status inquiry is widespread, more than 70%. However, the use of intelligent automation, particularly RPA in this area, is just beginning to show its impact.
RPA is being used successfully in this part of the revenue cycle. For government payers, claim status updates driven by RPA lets providers know with certainty and much more quickly when, and for how much, a claim will be paid.
Actionable or enhanced claim data processing represents another area where RPA can have a significant impact on revenue collection. For example, it is not uncommon for account information to be lumped together by payers, often simply because of incorrectly documented subscriber identifiers. Obtaining remit information from payers through an RPA-driven system to supplement claim status responses provides a more comprehensive view of the claim.
Adding remit data such as patient responsibility to claim status responses will then drive higher call-avoidance rates. If a process such as this is enhanced with RPA, the impact on productivity and ability to collect even low-balance accounts increases dramatically.
Denials follow-up: Analytics drives automation
Each year, almost 10% of claims are denied by payers.[e] With the cost to recover these denials and underpayments approaching $120 per claim, it is easy to see how a portion of the $266 billion in annual waste can be reduced by thoughtful and well-designed application of intelligent automation.
When informed by advanced analytics, RPA offers a highly effective way of reducing denials and underpayments and streamlining the effort and resource requirements to follow up on exceptions. Providers should expect a significant reduction in resources required to process denials when RPA is fully implemented with a sophisticated analytics program.
Low-balance accounts — those under $2,000 — are extremely labor intensive and difficult to collect through conventional manual processes currently in use at most providers. To maximize the benefit of RPA in this part of the revenue cycle, healthcare organizations should apply analytics to assess the likelihood of payment and the most effective use of staff resources to follow up on those accounts that are most likely to pay sooner and at a higher rate. Finally, the combination of advanced analytics and automation can help predict when payers will satisfy a claim.
By combining advanced analytics with RPA, insights can be gained at the payer and procedure level that allow better management of the front-end resources and improve the efficacy of the automation on the front end. This virtuous cycle of feedback (as a contrast to a vicious cycle) and continuous improvement throughout the revenue cycle will improve profitability and cash flow for providers of all sizes.
Intelligent automation offers providers the opportunity to transform many critical functions in the revenue cycle. In addition to the hard benefits of improved quality, reduced cycle time and reduced cost to collect, this technology will uncover opportunities to retrain resources for higher-value work, improve staff morale and reduce turnover. All of these benefits add up to a significant reduction in the overall administrative waste in a health system.
[a] Shrank, W., Rogstad, T., Parekh, N., “Waste in the U.S. Health Care System Estimated Costs and Potential for Saving,” JAMA Network, October 2019.
[b] CAQH, CAQH Index 2018, 2019
[c] “Front-end revenue cycle processes leading cause of denials,” Revenue Cycle Advisor, October 2017.
[d] Bruno, J., Johnson, S., and Hesley, J., “Robotic Disruption and the New Revenue Cycle,” hfm, Sept. 2017.
[e] Nilsson, E., “4 Strategies for an AI-driven approach to improve revenue cycle performance,” hfm , September 2019.
About the Author
is vice president at HGS, Philadelphia, and a member of HFMA's Metro Philadelphia Chapter.