What is RPA?
Robotic Process Automation (RPA) is the application of computer software, or “robots”, to execute specific tasks within a process. RPA has gained traction in recent years as a way of automating tedious and repetitive tasks. These tasks can range from processing transactions, manipulating data, or communicating with other systems. Most RPA software programs are designed to capture and mimic the activities of a human user within or across applications. These programs can execute repetitive tasks more quickly, accurately, and tirelessly, increasing efficiency and reducing the margin for error.
However, RPA is more than just replacing human effort with technology. Its main purpose is to optimize business process designs by automating mundane, rule-based tasks that scale elastically with business demand. This frees users to focus on higher-value tasks involving analysis and decision-making, such as tasks that require the use of emotional intelligence, reasoning, judgement, and interactions with others. As technology advances in machine learning and artificial intelligence, RPA will begin to tackle these more ambiguous human tasks beyond the mundane repetitive ones.
Regardless of the maturity of the technology or use case, RPA requires proper design, planning, and governance to succeed. Companies that approach it from a business impact perspective must rethink their entire business from a digital perspective. This involves taking an end-to-end view of organization functions and processes. Viewing the business this way presents a different framework for how and where to deploy RPA.
Benefits of RPA
From cost reduction to compliance improvement and increased customer satisfaction, retention, and market access, RPA has the potential to benefit various value chains within an organization. According to a study by McKinsey and Company, organizations that embrace RPA as part of their digital transformation are expected to see a return on investment that varies anywhere from 30 to as much as 200 percent in the first year of implementation into full scale production.
Implementation is typically cheaper for processes that require partial or complete redesign to improve compliance, as retraining human users is often time-consuming or requires additional resources. Automation has the ability to scale to business volume, and these robots do not experience the same learning curve that human operators do. They can evolve quickly with their designer’s requirements, do not feel fatigue, and do not require the additional, fully loaded costs of employees. The additional cost of infrastructure resources and licensing for operationalized RPA solutions is typically less than the cost of having a human execute the same tasks.
Aside from the benefits of automating current processes and process redesign, properly established RPA capabilities can also support organizations experimenting with operationalizing new business capabilities or establishing themselves rapidly in emerging markets. Combined with the elasticity of a cloud infrastructure, organizations can quickly scale up or down based on market feedback, reducing bottlenecks in business service processes (i.e., payments, customer services, or other non-physical production processes).
RPA in managed markets for life sciences
RPA is a technical solution that has broad applications in almost any industry. It is meant to automate tasks within a process that are stable, rule-based, repetitive, and usually high volume. There are various use cases within the life sciences industry that are good candidates for this type of automation.
RPA can automate specific tasks within processes such as:
- Medicaid claims processing
- Managed care claims processing
- Chargebacks submission processing
- Master data management and maintenance
- Contract maintenance
Although there are general industry standards for these business processes, different applications may be in use at different companies, and each company will always have some unique nuances to their business practices. Thus, as with other applications of RPA, a full business process analysis should be completed in order to optimize a process and properly identify the appropriate tasks to automate.
For example, Medicaid claims processing is a fairly standard process that is still executed manually by most pharmaceutical manufacturers. Many of the overall steps within the process are repetitive and rule-based, making them excellent candidates for automation through RPA. A look at the “As Is” and potential “To Be” states of the process after automation shows a significant reduction in the steps that require human intervention, increasing efficiency of the claims processing and freeing them up for other tasks.
Figure 1: Medicaid claims process after potential automation with RPA.
In an upcoming blog, we’ll take a more in-depth look at a strategic approach for identifying use cases ready for automation.