Revenue cycle management in healthcare is not solved by intelligent automation alone. It would be best not to use it for every project, as it provides immediate ROI. Furthermore, the automated process within the healthcare industry is quite different from that in other industries. There are typical myths to avoid while learning how to apply intelligent automation into your revenue cycle processes.
One prevalent misconception is that the revenue cycle must be automated. The first stage in automation is determining which jobs should be automated and performed by humans. Understanding the intricacies needs a comprehensive understanding of the revenue cycle process, including task relationships and impacts. Based on the complexity and depth of operations in the revenue cycle and the connections between providers and payers to assist the outcome, 100 percent automation in any business process is impractical and should never be the objective. As a result of technologies like machine learning, optical character recognition, natural language processing, and RPA, new processes have become possible to automate.
IA is also falsely claimed to be applied uniformly across organizations in the revenue cycle. Complexity is unavoidable in healthcare organizations, but because procedures, goals, and systems differ, IA must be adapted to each context. The automation process should take into consideration your specific IT procedures and interact with essential internal systems such as your EMR or patient accounting system. The precise capabilities of a solution does not imply that it will solve your difficulties. Your scenario modeling should show how it will affect your revenue cycle outcomes. So standalone or bolt-on technology, which lacks process optimization and integration, is rarely the answer. For example, if you speed up a faulty process or add bots, you may observe a temporary boost in efficiency. However, if patient data is maintained in many systems with limited interoperability, so-called automation has minimal use. Workarounds will be required by your personnel, and your operational overhead will continue to climb.
The claim that Intelligent Automation is quick and easy to implement, and that ROI is quick and easy to realize is frequently made. Easy and fast implementation is more achieved through standard operating procedures. For complicated healthcare systems, standard implementations do not work. The same may be said for instant outcomes that boast high-tech capabilities. The opportunities for quick wins are highly valued, but they do not improve performance in the long run. There may be an improvement in the performance of the revenue cycle or an operational lift, but sustainable results require automation that’s more strategic and tailored to your particular customer scenarios.
Implementing and maintaining automation is just as challenging as developing the right solution. A large budget and a large number of resources would be required for in-house deployment. To change the game, health systems must implement applications designed to work at scale. To achieve IA success, the focus must be on strategy, holistic solutions, and scalability. Instead of relying on standalone technologies, quick-win marketing, and engagements that are only one-sided, firms must collaborate with specialists who can adopt and manage automation to increase operational capacity. With platforms integrating machine learning, natural language processing, optical character recognition, workflow orchestration, and other powerful technologies, your health system can address more revenue cycle challenges via automation to improve margins.
