In many parts of the world, healthcare is an inefficient, top-heavy industry. In the United States, roughly 35% of the close to $4 trillion spent on healthcare goes to administration. Think about that: Around a trillion dollars is spent on healthcare administration in the United States every year. Large contributors to this broken system are the fragmentation of healthcare providers and the closed, proprietary nature of many applications used in the clinical and financial sides. Furthermore, incentives are aligned in a way that causes poor interoperability and data mobility across systems, institutions, and companies. This results in human workers acting as information routers, manually moving data between systems by selecting, copying, and pasting. This robotic work is tedious and unsatisfying, and it also has a high error rate because people lose their focus and interest when they do monotonous jobs.

Robots to the rescue

Fortunately, there is a technology solution to this problem. Enter robotic process automation (RPA), which uses software agents called “robots” to emulate the actions of humans interacting with digital systems in order to execute business processes. RPA robots use applications’ interfaces to capture and manipulate data just like humans do. RPA robots can also interpret information, trigger responses, and communicate with other systems to perform a wide variety of repetitive tasks. This is exactly what machines (even software-defined ones) excel at. They never get bored or frustrated, lose focus, or make mistakes.


Here are some of the tasks that RPA robots can perform quickly and flawlessly:

  • Mimic most human user actions
  • Log into applications
  • Connect to APIs
  • Read from and write to databases
  • Move files and folders
  • Copy and paste data
  • Fill in forms
  • Extract structured and semistructured data from documents
  • Open emails and attachments
  • Scrape data from web browsers

Crucially, deploying RPA robots does not require existing applications to change in any way. This means that healthcare institutions can realize benefits with minimal up-front investment and no disruption to existing processes in a way that is highly scalable and adaptable to changes in the business environment. Gartner predicts that 50% of healthcare organizations in the United States will invest in RPA by 2023, driven by the accelerated need to reduce costs and optimize resources triggered by the COVID-19 pandemic.

How to maximize the benefits of RPA

To get the most out of an investment in RPA with software agents that can work fast and around the clock, the infrastructure supporting the application environment must be up to the task. Minimizing latency and eliminating downtime become even more important when robots are working around the clock, 7 days a week. Access to data from different applications is simplified and sped up if that data resides in a consolidated environment.


With NetApp® ONTAP® adaptive quality of service (AQoS), you can deploy all your healthcare applications in a single clustered storage environment that scales out. The result: You can eliminate data silos, guarantee peak performance, and lay a solid foundation for a successful RPA implementation. Adding NetApp Cloud Insights to the mix strengthens the foundation even further by securing your data and increasing uptime through early detection of ransomware and automated responses to threats. Cloud Insights uses machine learning to detect greedy or degraded resources, enable you to customize targeted and conditional alerts, and a host of other features.


We’d love to have a conversation with you about RPA. Contact us at

Esteban Rubens

Esteban joined NetApp to build a Healthcare AI practice leveraging our full portfolio to help create ML-based solutions that improve patient care, and reduce provider burnout. Esteban has been in the Healthcare IT industry for 15 years, having gone from a being storage geek at various startups to spending 12 years as a healthcare-storage geek at FUJIFILM Medical Systems. He's a visible participant in the AI-in-Healthcare conversation, speaking and writing at length on the subject. He is particularly interested in the translation of Machine Learning research into clinical practice, and the integration of AI tools into existing workflows. He is a competitive powerlifter in the USAPL federation so he will try to sneak early-morning training in wherever he's traveling.

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