For the Record: A Look Inside Intelligent Automation

Sean BarrettFebruary 15, 2021


revenue cycle automation

Over the past five years, there has been significant buzz in the health care industry around automation, including countless articles advertising how it can transform the revenue cycle while delivering major cost savings. For the most part, the fanfare is warranted; automation can help generate significant operational improvements and cost reductions for health systems.

 

The variety of use cases that apply to the revenue cycle’s complex processes, disconnected systems, and large labor force serve as a rich hunting ground for automation technology that can accelerate and standardize these time-consuming workflows and enable essential organizational change.

 

Nevertheless, a Bain & Company survey found that almost one-half of business executives indicated that automation initiatives within their companies have failed to produce the bottom-line benefits originally anticipated. The challenge many providers are facing is that they’ve invested in off-the-shelf solutions or taken a piecemeal approach to automation and have yet to see impactful results within their revenue cycles, addressing tactical pain points instead of identifying ways to change the game and automate large-scale processes.

 

The wealth of opportunity to automate revenue cycle functions may be its own demise as organizations often gravitate toward quick-win traps instead of prioritizing strategy, scalability, and holistic solutions.


An intelligent automation (IA) platform incorporates a combination of technologies such as machine learning (ML), natural language processing (NLP), optical character recognition (OCR), robotic process automation (RPA), and workflow orchestration that broadens the potential use cases while empowering humans to work alongside these digital assets. To attain lasting financial results, providers should consider how this layered technology approach can increase the number of revenue cycle processes that can be automated.

 

The Basics

As providers become more aware of potential barriers to success, it can be difficult to know where and how to start applying automation technology. Knowing which technology to deploy is very much dependent on the task, as well as the information the provider is working with. For example, if working with unstructured data such as an image file, medical record, or clinical chart, NLP/OCR technology is best suited to preprocess data and feed them into a subsequent automation. When dealing with structured data, ML can be used immediately to assess trends and determine the best way to complete a transaction. If completing routine manual transactions such as insurance verifications and payment postings, RPA may be the best choice because it allows digital workers to perform these activities more quickly.

 

However, for better outcomes, organizations need more than just one of these levers; they require a full complement of technology to automate the multiple components that make up a complex revenue cycle process, as opposed to marginally improving certain pieces.

 

A comprehensive illustration of applying a series of automation technologies to a revenue cycle process is correspondence management. The “paper processing problem” is a classic health care dilemma that consumes large amounts of manpower to process correspondence from banks—letters, checks, and explanation of benefits—and manually pulls data from these files for entry into an accounting system or workflow drivers for downstream processing.

 

By applying each of the technology levers to targeted aspects of this process, the IA delivery platform can automate the following actions:

  • RPA retrieves files from bank lockboxes.
  • ML identifies which files are needed for billing correspondence and sends them to OCR/NLP technology.
  • OCR/NLP extracts patient-level account data from files and converts them into a standard format.
  • RPA receives standard formats and attaches necessary documents to patients’ accounts.
  • Workflow orchestration enables escalation to human users for exception processing.

 

This automated workflow simplifies and standardizes the overall correspondence management process from start to finish and tackles each distinctive part of the workflow with the technology lever that is best suited for completing that work accurately and proficiently while maximizing human workflow efficiency.

 

While RPA, ML, and OCR/NLP technology can generate significant process upgrades independently, they can provide better solutions within the revenue cycle when they are deployed in a unified way that removes variation and allows work to be completed faster with fewer errors. By automating the majority of the correspondence management workflow and escalating the remaining tasks that could not be automated quickly with a reasonable return on investment, delivery is accelerated and incremental benefit is more readily achievable.

 

Furthermore, while most revenue cycle processes can be fully automated, certain tasks such as exception handling and complex patient cases/interactions still need to be handled by humans. Having this unified “human in the loop” cadence reduces reliance on humans to carry out time-consuming tasks and enables them to focus on more strategic work and provide more helpful patient interactions.

 

Benefits

When applied correctly, the IA platform can improve net revenue capture, deliver cost reductions by automating manual rules-based revenue cycle tasks, and enact more predictable reimbursements. Because the technology can consume large volumes of data and create learning algorithms that capture the best way to complete a transaction, it also gives health systems access to better decision-making resources. With these algorithms, the technology is able to learn patterns in historical outcomes and use this knowledge to make decisions on behalf of the operators.

 

For example, when reviewing claims data, ML uses historical reimbursement trends to predict potential write-off. Then, the integrated workflow platform either flags high-priority items for operators to manage or provides the lower-dollar write-offs to RPA/digital workers to process.

 

Establishing uniformity on the back end of the revenue cycle, which includes removing frequent errors that cause interruptions and interoperability challenges, lowers overall cycle times and ensures that your health system is performing on a consistent level, which helps improve overall financial margins.

 

By being able to run many revenue cycle processes 24 hours a day, the IA platform creates a level of standardization that offers leaders a better line of sight into daily operations and helps them determine where opportunities exist to further increase revenue streams (eg, eliminating revenue leakage and maximizing patient volume). Because they are now receiving a more seamless consumer-facing experience that is no longer impacted by inefficient handoffs, frustrating setbacks, and potential billing delays, this operational consistency increases patient satisfaction.

 

When it comes to using technology, health systems need more than a one-size-fits-all automation solution. They need a platform that thoughtfully deploys technology components to maximize resources and achieve return on investment. Deploying a few bots to complete daunting tasks or using bolt-on technology to speed up flawed processes will only produce more workarounds for staff members while operating expenses continue to increase.

 

With so much financial uncertainty in the health care industry, it’s important to look past the quick automation wins that are bound to deliver disappointment. Having a multilayered IA platform that can holistically be applied to the revenue cycle in a tailored, meaningful way and enhance each step in a workflow is how health systems can take control of their daily operations, see results, and ultimately save money.

 

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Author Bio: Sean Barrett is the Senior Vice President of Digital Transformation at R1 RCM. He joined R1 in 2018 and currently oversees R1’s core product management, automation, and machine learning functions. Prior to R1, Sean spent 14 years at Deloitte Consulting focusing on serving clients primarily in the healthcare provider segment-leading operational performance improvement and technology-driven transformation engagements at many of the largest health systems in the country.