What Is Robotic Process Automation?
RPA is a type of business process automation technology allowing organizations to emulate and integrate human interactions within digital systems for more efficient business processes.
Using RPA tools, a company can configure software, or a “robot,” to capture and interpret applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. RPA scenarios range from something as simple as generating an automatic response to an email to deploying thousands of bots, each programmed to automate jobs in an ERP system.
Compared with traditional IT solutions, RPA enables organizations to automate processes at a fraction of the cost and time previously required. The technology is also nonintrusive by nature, using an organization’s existing infrastructure without disturbing its essential systems.
The Current Use Cases of RPA in a Clinical Setting
McKinsey predicts that for a majority of occupations, 30 percent of tasks can be automated; RPA is one way of doing that.
RPA robots are capable of mimicking almost any predictable human interaction, allowing them to log in to applications, move files, fill in forms and more. Leveraging this technology in healthcare presents opportunities that can free up clinicians’ time and enhance care delivery.
KPMG lays out some common use cases, which include:
- Improving the healthcare cycle: Providers collect vast amounts of data from their patients each day — from personal information to treatment cycle details. With the help of RPA software, healthcare organizations can extract and optimize patient data more effortlessly. In communicating with other digital systems, RPA software can manipulate collected data to generate analytics that offer clinical staff valuable insights to help them make more accurate diagnoses and offer tailored treatments to patients.
- Scheduling new patient appointments: When a patient fills out a new patient appointment request form online, RPA robots can help to scan the incoming data, build out a condensed report and direct the appointment request to the correct work queue based on its defining attributes — including location, diagnosis and insurance carrier. This presents clinics with a cost-effective method of scheduling new patient appointments, leading to increased satisfaction for both patients and providers.
- Simplifying claims processing: Most claims involve processes such as data input, processing and evaluation. Whether those steps are conducted manually or by generic software, the process is often time-intensive and error prone — sometimes, even having a major impact on cash flow. RPA helps streamline the claims process by speeding up data processing and simultaneously reducing the number of errors. It can also help to address recovery of revenue that might have been written off.
These are just a few examples of how this technology can improve accuracy across an organization and fuel profitability. Allowing RPA software to handle business processes will not only enhance clinical workflows but will return coveted time to staff and allow tailored treatments for patients.
What to Consider for a Successful RPA Implementation
As with any other technology initiatives, RPA implementations can fall short for a variety of reasons; among them, lack of training, poor use cases, excluding key stakeholders and more.
For a successful RPA deployment, CIO suggests organizations follow several best practices, such as doing ample research, educating staff, outlining a strong use case and reviewing security.
Also, consider that RPA is a new technology with growing capabilities. Organizations should determine first what problem they hope to solve with RPA, and what products might be best for achieving that goal.
When it comes to developing an RPA strategy, selecting a vendor and implementing solutions, consider seeking advice from outside experts, who can help with each step of the process. In selecting the right partners to implement this technology appropriately, organizations will see fewer errors and lower costs — both of which can lead to improvements in clinical outcomes.