July 12, 2021
Confused between solutions touting robotic process automation (RPA) versus artificial intelligence (AI)? Not sure where to start with developing an AI or automation strategy for your hospital or health system?
Healthcare industry adoption of AI is rapidly expanding; healthcare organization executives have seen the need for greater efficiency, cost reduction and data management across their organizations. A recent report from Sage Growth Partners highlighted that in 2020, 90% of hospitals had an AI strategy, up from just 37% in 2019. But with various technologies and terms floating around, it can be hard to differentiate solutions and understand what your organization really needs to take advantage of these innovative technologies.
Robotic process automation is merely one small part of a comprehensive AI strategy. Healthcare executives need to understand a broader vision of AI — one that includes RPA — to create and implement an effective strategy for their health systems.
The term ‘artificial intelligence’ actually encompasses a wide scope of technologies, each designed to replicate certain human activities. Here is a quick overview of some of the specific AI subtypes:
The pandemic showed healthcare systems and healthcare providers the need for new, innovative solutions that fundamentally change how they function and streamline operations. Small fixes or increased hiring won’t cut it. To recuperate from the financial and human workforce challenges, health systems need AI to reduce time-consuming or repetitive tasks and improve business processes. And they need it to deliver on its promises. As you can see from the (non-exhaustive) list above, artificial intelligence has multiple components and solutions to offer a health system. It is so much more than just RPA.
To realize its full potential, health systems need an overarching AI strategy to align internally. Without a strategy in place, health systems risk internal disagreements, competing priorities, and a tangled web of solutions and partners that hold the entire enterprise back.
This can especially be a problem where RPA is involved. One of RPA’s benefits — its simplicity — can also be one of its biggest drawbacks. Internal teams can easily create simple RPA solutions themselves, but then maintenance and governance become large issues as the technology’s presence grows. When an RPA solution breaks from something as simple as a software update (which happens often with RPA) or human error, who is responsible for updating it? How is the cost and ROI of a project calculated? Problems such as these will grow and eat away at the confidence leaders and employees have in the feasibility and potential of automation. On the other hand, with an AI strategy in place that appropriately aligns technologies and use cases, health systems will see greater AI success.
When you begin to research applications of RPA in healthcare, countless automation opportunities across the enterprise pop up. RPA can be applied to HR, the hospital supply chain, the healthcare revenue cycle, even the pharmacy. But since RPA is limited, it is best combined with AI to automate bigger and more complex manual processes, bringing more benefits to the organization.
For example, RPA implementation and AI can be applied to many steps in the healthcare revenue cycle, from eligibility and insurance verification checks to claims management. When you choose a holistic solution instead of creating an independent RPA bot for one step, each part of the process works together. This creates a seamless and streamlined healthcare revenue cycle, reducing claim denials and providing actionable insights that improve your bottom line.
Similarly, in the hospital supply chain, RPA may be able to do things like update physician preference cards. But in this case, when you pair RPA with AI, you can actually optimize them. AI that uses ML and RPA can parse all the cards and analyze product mixes to find the standardization opportunities hidden in the data, and then update the cards once changes are approved. Artificial intelligence can also automate inventory management, medical supply spend analysis and other supply chain activities, saving employee time while also reducing costs.
When you look at RPA as just one small puzzle piece in your artificial intelligence strategy, you start to see the power and benefit that even simple automation can have when combined with the network intelligence of an AI solution. The benefits of RPA are multiplied when you connect it to artificial intelligence. The data that is generated, the insights that are captured — you start to develop an entire system underlying your hospital operations that can generate value today and increase in value tomorrow. As your AI learns more, it can do more for your health system.
Olive isn’t just an RPA software. She’s an artificial intelligence partner that brings intelligent automation and cybernetic solutions to every corner of the hospital. Explore our solutions and see how Olive can improve your financial performance, deliver transformation at scale, and lead you to a fully connected enterprise.
Healthcare looks quite different than it did just a few short years ago. The COVID-19 crisis has created a distinct “before” and “after” for the world, and especially for healthcare.
Artificial intelligence (AI) is revolutionizing healthcare data analytics and changing the way we predict, learn and act based on insights gained through AI-powered data models.