Understanding the Value of RPA, AI and ML & Euclid Moments

Understanding the Value of RPA, AI and ML

February 08, 2021

While transactional businesses like retail focus on winning a single sale with a customer, and process businesses like manufacturing focus on efficiencies in every step, healthcare is a relational industry where long-term relationships are essential. In today’s device-driven world, health systems need a comprehensive digital strategy to build relationships with patients that extend beyond the four walls of a hospital or clinic.

The need for long-term relationships extends beyond the provider to patient. The vendor-provider partnership requires that as well. That doesn’t remove the need for a digital strategy. In fact, to enable the provider to build those needed patient relationships, vendors must facilitate and enable such connections to seamlessly exist through technology. The critical clinical side of a healthcare operation is challenging enough. Having a business operation that is cumbersome, out of date, riddled with manual process, and slow to respond to industry and government changes becomes a significant burden. Such dysfunctional administration typically reduces healthcare systems’ quality of care, while weakening patient relationships and creating financial problems that can no longer be afforded.

The right software properly implemented and utilized enables healthcare organizations to manage and secure patient data and automate and streamline administrative tasks (source system interoperability, RCM functions, trading partner communications, etc.) and frees providers to deliver improved patient care more efficiently.

The growth of RPA (Robotics Process Automation) is providing healthcare organizations the ability to handle menial tasks and raise their operational efficiency. That in turn, enables them to build the much-needed provider-patient relationships. The technology, as the Association for Intelligent Information Management notes, involves software tools that automate human activities that are manual, rules-based, and repetitive. RPA “bots” are being used to expedite functions throughout provider organizations. Some common uses include patient data extraction, routing, and analysis, and appointment scheduling and claims processing.

In an analysis published in the The Wall Street Journal, David Yarin, principal, Deloitte & Touche LLP asserted that RPA tools often provide “a tremendous opportunity for healthcare and life sciences organizations to alleviate some of the administrative burdens that plague healthcare delivery while also assisting clinicians in making decisions about patient care”. Still, RPA has been met with some resistance in healthcare. It appears the resistance stems from not knowing what RPA really is and can accomplish. I’ve spoken to Chief Technology Officers in the past 2 years who told me that “RPA is more of an automotive industry technology – not healthcare”. That stereotype, pigeon holes RPA into a physical robot world, i.e., an automotive assembly line.

Some of you may remember Robot B-9 from the television series Lost in Space. One of the more memorable phrases B-9 recited was “Danger, Will Robinson, Danger!” It could be that healthcare executives are still feeling that RPA technology contains some inherent danger because it has the word robotics in it versus COBOL, JAVA, C++, or SQL it is less known, and therefore less trustworthy. If so, let’s consider R2D2 and 3CPO. Never before were there more dependable and loyal “bots”. The industry is moving into an era where AI(artificial intelligence) and RPA will also become household names, at least until antiquated and displaced by more efficient and cost-effective technology. When in-person conferences (or new effective replacements) resume, that process will be greatly expedited.

That said, there will be bots in software that providers and service vendors aren’t even aware of because they are imbedded in the application. When sitting in on a demo an RCM veteran may be impressed by the way the software is designed and operates just as a biller would want it to operate. That software most assuredly has bots operating in the background to handle otherwise time-consuming tasks that make billing personnel more efficient, productive, and effective.

AI and machine learning (ML) algorithms are steadily evolving and maturing. ML is a sub-part of AI and is akin to Narrow AI or (ANI). That AI provides artificial intelligence that enables processes, based on instructions to occur. Narrow AI is a collection of technologies that rely on algorithms and programmatic responses to simulate intelligence. There is no actual thinking occurring with either ANI or ML. GAI or General Artificial Intelligence is designed to be able to think on its own to the degree that it matches or surpasses human capability. I am not certain Robot B-9 had GAI (though not reality) but surely R2D2 and 3CPO did.

All of this technology can become dizzying to the average healthcare professional. Since it isn’t all yet perfect, it can also be suspect when considering major acquisitions or projects that may include RPA, and AI/ML. But sometimes both are needed and if improvement can occur, money can be saved, revenue can be collected sooner, and patients better cared for it is worth considering. Think of it in this example of patient enrollment – Jane Doe data comes in from the plan source system electronically and her birthdate is July 3, 1990. The provider EHR source system has the same Jane Doe with a birthdate of July 4, 1990. Now what? AI/ML could give an application the capability of comparing the data from both source systems and illustrating it in an automated edit workflow. RPA may be instructed to use the EHR system data when there is a difference between the two. It then would correct the birthdate to July 4 and indicate that change on the edit list audit trail. That may be a bit of an oversimplification, but it doesn’t take long to figure out the savings and efficiency of such capable software. Technologies such as RPA, AI and ML can help healthcare organizations collect and translate patient and transactional data into meaningful, actionable formats; streamline compliance-related processes; and shift employee responsibilities away from menial tasks they now perform, thus paving the way for them to focus on patient care quality, research, and other mission-critical activities.