By Joe Polaris, senior vice president of product and technology, R1 RCM

Don’t let the term “artificial intelligence” (AI) mislead you. Even when individuals have some knowledge on the subject, too often AI conjures images of robots taking over the world — or beating us at our own Jeopardy!® games.

In reality, AI should simply be thought of as a way to meaningfully transform ordinary tasks through technology.

Applications within the healthcare revenue cycle offer a prime example. Being that so many complex tasks are part of every revenue cycle management (RCM) process, the integration of AI can help healthcare organizations improve operational efficiency and reduce costs — all while simultaneously delivering the kind of personalized patient financial experience the industry is calling for.

To understand how these efficiencies are created, let’s look at advances in two types of technology: robotic process automation and cognitive automation:

* Robotic process automation (RPA). RPA technology is not new. It has existed for decades, and it entails developing software programs capable of executing specifically defined processes. Under the RPA umbrella is the concept of “user emulation,” which is when a robot emulates what a person would do to complete the same process. In other words, RPA lets robots do work.

 While in theory RPA should permit robots to replace mundane tasks, the real world introduces a pretty substantial curve-ball: variation. The problem is that historically robots have been known to “get stuck” on tasks that aren’t exactly identical time after time — and very few tasks have zero variability, especially in healthcare revenue cycle. So, RPA has had limited RCM application in the past.

Fortunately, recent years have seen huge advances toward greater flexibility. More complex programming languages and technology solutions let us abstract away questions about “what’s the variation going to be?” and instead train robots to deal with variation itself — and keep working.

* Cognitive automation. Whereas RPA enables the completion of pre-defined steps, cognitive automation allows machines to process information more “intelligently.” It includes a range of technologies such as machine learning, AI, natural language processing (NLP) and optical character recognition (OCR). With these, robots gain such capabilities as recognizing meaning in written or spoken words — skills previously only within the purview of humans.

Technologies that combine RPA and cognitive automation add greater efficiency into core RCM processes — even those that include variation. Deciding how best to apply the wide variety of available technologies rests on two factors: the complexity of the transaction and whether or not the transaction is patient-facing.

Case Examples

No matter where you look in the healthcare revenue cycle, use cases for a technology suite that blends RPA, cognitive automation and analytics are abundant. One common application can be found within the claims denials workflow.

Claims denials are still treated as a fairly manual process within many healthcare organizations. Although electronic claims scrubbers catch claims at risk for rejection or denial, a person must review the list of at-risk claims, identify the problems and route each claim to someone with the right expertise to resolve the issue.

By contrast, technology that leverages RPA and cognitive automation can intelligently route the claims to the appropriate end user, triage and disposition the action or complete it entirely without the need for a human intermediary. This not only accelerates workflows, but it also reduces human variation and error by standardizing the routing process. Furthermore, staff resources can be freed to work on more engaging tasks that require a higher degree of problem-solving skills.

On the patient-facing front, digital self-service platforms that join RPA and cognitive automation with analytics are helping healthcare organizations personalize the patient financial experience. Using data-driven algorithms to predict a patient’s ability and willingness to pay their bill, technology can then tailor payment options, plans or financial assistance as appropriate to each person. AI-based chat bots can even be used to answer basic questions the patient might ask while in the payment process. To a patient who asks, “Have you billed my insurance yet?” a robot could reply, “Yes. We billed your BCBS insurance $500 on Dec. 15, 2018, and they paid their portion of $400 on Dec. 23, 2018. Your payment responsibility is currently your co-pay of $100.”

Advantageous Intelligence

RPA and cognitive automation technologies enable robots to perform tasks, as well as continually optimize them. Through this technology, we can now manage RCM processes even if they are variable, and even if they aren’t 100 percent automatable. In addition, advanced visualization tools give us the power to monitor the performance of robots doing a multitude of tasks at scale, so we can further optimize RCM workflows as needed.

The more healthcare RCM can use AI to automate complex tasks, the better, since these advancements are significantly enhancing workflows and how our overall workforces perform and operate together. As a result, we can look to see improvements in denial rates and operating costs, as well as the overall patient financial experience.

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Joe Polaris’ deep front-line revenue cycle experience fuels his passion for building innovative end-to-end processes that increase financial stability for hospitals and health systems. As senior vice president of product and technology at R1 RCM, Polaris recognizes the importance of financial advocacy for health systems, providers and patients alike. He understands how digital transformation and transparency in the healthcare revenue cycle not only drive meaningful revenue for facilities, but also act as major contributors to an empowered and positive patient experience.

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