A year after operating margins dipped into negative numbers and YOY revenues decreased as much as 50%, hospital leaders need solutions to help them achieve a financial 180. They’re certain automaton can help them get more out of their revenue cycle; according to KPMG, early adopters believe artificial intelligence (AI) will improve numerous areas such as electronic health record (EHR) management. More than 90% say it will improve the patient experience.
Automation has the potential to transform the revenue cycle, enabling significant improvements from end to end, but not every hospital will reap major benefits. Overcoming RCM’s complexities may prove difficult, especially if hospitals fall prey to common missteps.
With RCM’s inherent challenges – evolving consumer-driven demands, integration issues, complexities from big data, a third-party payer system and the shift to value-based care – automating it can be daunting. Healthcare financial leaders may not be able to implement and manage automation in a way that delivers the expected ROI. A number of common missteps can hinder results, creating additional work and diminishing expected returns.
Read the recent HIT Consultant article, Misconceptions When Applying Revenue Cycle Automation, to learn more.
Most common missteps in RCM automation stem from one key error: Failing to take a 20,000-foot, holistic view of the revenue cycle and develop a strategy for scalable, end-to-end intelligent automation.
It may be easier to address tactical or singular pain points, or more appealing to gravitate toward exciting “quick-win” solutions. But sustainable, game-changing results come from leveraging a combination of technologies such as machine learning (ML), robotic process automation (RPA) and natural language processing (NLP) to create a layered intelligent automation (IA) approach.
A well-developed IA strategy enables you to layer different automation technologies, thus increasing the number of processes that can be automated. Within the complicated workflow of the revenue cycle, this fuller complement of technology lets hospitals use the ideal type of automation for each process or task rather than having to rely on a “one size fits all” approach.
To provide even more value, R1 leverages an IA ecosystem. The IA ecosystem uses the layered approach but also includes an integrated workflow platform for an added critical benefit: Digital and human workforces are able to work seamlessly alongside each other. Together, these two benefits enable hospitals to truly move the needle in multiple KPIs such as cost to collect and net patient revenue rather than making marginal improvements.
Download our intelligent automation infographic to learn more about R1’s IA ecosystem and how it benefits hospitals.
In R1's IA ecosystem, the integrated workflow platform orchestrates revenue cycle workflow and ensures seamless hand-offs. It uses technology “levers” such as RPA, maximizing the efficiency of these technologies as well as human workers. While most typical RCM processes can be fully automated, the integrated workflow platform knows which tasks require human intervention. It directs tasks to the best worker – either a human or a digital lever – and uses exception handling to route requests correctly.
In highly detailed workflows with numerous variables - for instance, claims management - the integrated workflow platform is key to achieving the greatest efficiency and getting the most out of automation technologies. Here’s a closer look at three other technologies that are well suited for revenue cycle automation and included in our IA ecosystem:
Robotic Process Automation, or bots, are digital workers that navigate a system’s user interface as a human would. With RPA, highly time-consuming and labor-intensive work can be automated. RPA can perform routine, structured revenue cycle tasks with greater accuracy and speed than a human could. Ideal revenue cycle tasks for RPA include posting payments in a patient accounting system and downloading and attaching medical records to an appeal.
Machine Learning and Expert Rules is a form of AI that leverages algorithms to understand patterns in structured data and predict outcomes or actions. It can identify the most effective way to complete a transaction, ensure processes are carried out accurately, and learn where and how errors typically occur. ML can make numerous types of valuable predictions in RCM, such as the typical A/R follow-up activity and the likelihood of an account write-off.
Optical Character Recognition & Natural Language Processing can extract valuable information from unstructured data such as medical records, scanned documents and audio recordings. By replacing time-consuming work like sorting, transcribing and data entry, it can save revenue cycle staff a substantial amount of time. In addition, because it eliminates the need for transcription, it reduces opportunities for error.
Knowing what type of automation technology to implement and which tasks to automate can be challenging. To achieve the most ROI, remember you’ll need to apply IA to large-scale processes across the comprehensive revenue cycle. Also, when deciding what type of automation technology to use – or what lever to pull – you’ll need to consider both the task and the information you’re working with.
For example, with unstructured data such as an image file, medical record or clinical chart, NLP/OCR technology is ideal for preprocessing data and feeding it into a subsequent automation. With structured data, ML can assess trends and determine the best way to complete a transaction. Lastly, for routine manual transactions such as insurance verifications and payment postings, RPA may be the best choice because bots can quickly perform repetitive, routine tasks.
Intelligent automation is much more than any single lever. An IA strategy that enables you to tackle each distinctive part of the revenue cycle accurately and proficiently while also maximizing human efficiency can deliver many benefits, including reduced costs, improved decision making, increased staff productivity and a better patient experience.
Leveraging a fully developed IA ecosystem not only facilitates speed to value and high ROI, but also ensures you can pull additional IA levers or automate a greater volume of tasks as needed over time. As your health system grows – either organically or through consolidation – you’ll have a ready-made strategy and technology platform to scale appropriately. With the right IA strategy, efficiencies and gains will equate to much more than just financial savings and healthier profit margins. In addition, you’ll be able to increase patient, provider and staff satisfaction - and improve your competitive differentiation.
To realize these gains, you’ll need a partner with deep expertise in both IA and RCM. This partner should have a fully developed, proven IA ecosystem and framework for effectively managing your digital workforce after implementation.
Download our whitepaper to learn more about how to select an automation
partner and leverage an IA ecosystem for maximum ROI.
R1’s proven, extensive IA capabilities have enabled hospitals to achieve benefits such as a 20% reduction in cost to collect and 50% reduction in denials. We automate more than 32 million tasks annually. Interested in learning more? Click here to begin a discussion about how IA can reduce your costs, increase your profit margins and improve your team’s efficiency.
Philip Milsom is a Regional Vice President of Enterprise Revenue Cycle at R1 RCM.