The rapid advancements in automation are revolutionizing business operations for organizations in practically every industry. As automation technology continues to evolve and uncover new opportunities to showcase its effectiveness, healthcare companies are one of the industries rapidly discovering the benefits of its methodologies. While hospitals are projected to invest over $50 billion dollars towards artificial intelligence and robotic process automation solutions by 2020, some in the industry are only beginning to look into the potential of these solutions and their game-changing advantages. After spending time with over 300 revenue cycle and IT executives at Becker’s 4th Annual Health & IT Revenue Cycle conference, our teams at Olive were able to garner some details behind executives’ findings and concerns. The top 5 takeaways we found include a sense of hesitation regarding the ability to prove ROI, but also reveal that the most agreed upon application of AI will prove its worth most in repetitive high volume tasks like eligibility checks, authorizations, and claims.
Robotic process automation and machine learning are often the two technologies discussed the most when broaching this topic, but what is the difference between the two? Further, which of these two work the best for a given use case? We’ll discuss the details of both methods and help you answer both of those questions in this piece.
What is robotic process automation?
It’s quite common for robotic process automation (RPA) to be thought of as actual robotic devices performing operations on an assembly line or robot constructs like The Iron Giant and Transformers. However, robots exist in other forms as part of other technologies like soft-bots, AI, sensor networks, and data analytics. Fundamentally, the simplest way to describe RPA is that it’s a process by which a repeatable rule-based task is executed through an automation solution.
Operating within predefined rules and procedures, RPA solutions are able to complete an action through a machine that would normally require human interaction. Whether the task is in a factory environment or office space, RPA can help with the construction of a component for a finished product or even help office productivity by brewing coffee through Wi-Fi enabled coffee makers. Because RPA solutions require a thoroughly practiced, documented, and familiar procedure to fulfill its automation benefits, some believe it will eliminate the need for humans in some areas, however, that isn’t really the case. RPA is designed to handle the tedious repetitive tasks humans currently must do, enabling enhanced human productivity by allowing humans to focus on the more complex and creative tasks they excel at.
Sometimes considered to be the most basic form of AI, robotic process automation is best utilized in business practices that require little skill and are performed under set parameters including how often a task needs to be executed and within specified timeframes. In healthcare applications, RPA reaps loads of benefits by allowing skilled and/or specialized staff to focus their attention towards tasks that require human cognition and subjective decision making. There are often instances within hospitals where employees with clinical skills, such as nurses and aides, are tasked with additional tasks of insurance verification and data recording. While these responsibilities are expected within their roles along with other staff members, these duties are ideal for an RPA solution to tackle. Having these non-clinical jobs being addressed through automation allows for staff to concentrate their attention on their principal tasks better suited for their skills of patient care and advocacy.
Studies have already shown that the increase of automation in processing medical records and documentation has led to a 15% decrease in the odds of in-hospital deaths and administrations that have adopted RPA have seen a 200% ROI within the first year of use (Olive AI white paper). As the U.S. nears a projected shortage of 250,000 nurses by 2025, identifying and implementing automation solutions within healthcare infrastructures has become a much more pressing need thus allowing clinical staff to dedicate their abilities towards tasks exhibiting their skillsets.
What is machine learning?
Similar to robotic process automation, the primary objective of machine learning (ML) is to also have computing technology mimic human operations. However, where RPA is required to operate within a rule and process-based environment that limits decision making under unfamiliar situations, ML truly expresses its artificial intelligence as a learning resource exhibiting what most feel is the biggest characteristic of AI; adaptation. Simply put, RPA acts more like a straightforward resource that executes actions based on its configuration, which places it in more of a grunt perspective with little freedom to “think” outside the box or exhibit any learning abilities. Machine learning, on the other hand, autonomously improves its performance over time, like humans, as the system is provided with observational data and real-world interaction. Some have even made the comparison between the two as brains over brawn with ML being the former.
In the healthcare industry, ML also adds exponential benefits to administrations acting as the router between systems and data by automating repetitive high traffic tasks. Serving as its own employee within an organization, an ML solution utilizes its own credentials to access system databases to record and report patient information or EHR (electronic health record). By following the local credential structure, this allows for seamless integration into existing systems with little change to accommodate its inclusion and no additional workflows. For example, our Olive AI can be used to perform patient insurance eligibility checks. After reviewing the patient record and history from their respective EHR, Olive can assist with checking against insurance eligibility portals. With a baseline of information gathered, the system can then proceed to offer approved solutions, compare previously approved authorizations, schedule future appointments and post-visit follow-ups, and payments. Having this level of automation 24/7 365 days of the year empowers hospital and clinic staff to center attention towards their most critical role of patient care.
An article published in Healthcare IT News reported a prediction from IDC (International Data Corporation) that global investment towards AI solutions will jump 60% this year totaling $12.5 billion and then up to $46 billion by 2020. As automation continues its seemingly endless upward trend and creates countless prospective breakthroughs in practically every industry, machine learning continues to be a key proponent towards technological advancement.
So which one is better?
To answer this question, decision-makers and executives must first determine their most critical business needs that can be best be improved through automation. Overall, robotic process automation and machine learning are both invaluable solutions that are sure to drastically enhance business performance for any organization. Some businesses may opt to incorporate an RPA option in order to automate their easier low skill functions as this will require little effort to integrate and in the smallest amount of time. Other organizations have decided to use RPA as a starting point in their AI implementation with machine learning as their end goal for automation. Nonetheless, having discussed the capabilities of both RPA and ML, it seems the only one who can determine which is better for a business is the business itself based on their requirements and ultimately the option that will provide the highest ROI over time.
At Olive, we strive to build revolutionary artificial intelligence and robotic process automation solutions for the healthcare industry that layer in ML for a more robust robotic process automation solution. Our focus is on improving business productivity through automation of the error-prone and mundane tasks of healthcare administration so that staff can focus on patient care. Our efficient cost-reducing options continue to deliver immediate positive results with Olive AI overseeing repetitious high traffic processes and workflows. These specialized tools empower our customers with the freedom to let their teams express the creativity and empathy that only a person is able to provide. Please contact us to schedule a demo of our Olive AI and let us begin developing a solution that can address your automation demands and be your first step towards an AI environment.