Robotic process automation has emerged as an effective tool that businesses can use to generate operational efficiencies rapidly. However, it can also be used as the first step towards better data quality that builds a foundation for artificial intelligence and machine learning systems.
It can't come soon enough for hospitals, which are struggling
under the burden of shifting revenue and cost dynamics in an industry that
relies on processing large volumes of sensitive personal data. Indeed, at an
average of $408 per record, the cost of healthcare data breaches is at least
double that of other industry sectors (see Figure 1). The high price of a
breach is primarily because of the high notification costs and steep penalties
in the U.S. for healthcare information mismanagement. Better management and
automation would help significantly reduce the risk.
The health sector, in general, is far behind others in adopting digital technologies. According to our recent Digital Radar report, the healthcare industry scored an average of 41.25 out of 100 in our Digital Maturity Index. This score compares to an average of over 70 for technology companies.
Healthcare is an industry not considered to be a natural candidate for automation. It is a human-centric service where the personal touch significantly impacts patient experience and outcomes. However, there is still substantial potential for automation, given the extent to which data drives decisions and underlying systems. According to McKinsey, 36% of healthcare activities could be automated, and the majority of the opportunities for automation are around data collection and processing2.
Automation helps reduce today's data-related inefficiencies by improving data quality and consistency, as well as enabling standardized data structures that can create a unified view of performance across a hospital.
Hospitals that have implemented RPA have seen significant benefits. A Kentucky-based multiunit medical services centre upgraded to an integrated medical record and practice management system saved over 2,000 hours of manual effort. It took the RPA system less than 24 hours to do an error-free transfer of over 64,000 records from legacy systems to a new system3.
In Europe, a general-care hospital with 70,000 emergency visits and 300,000 outpatient visits annually faced labour-intensive challenges, was dependent on paper records for medical files and financial documents and had problems managing inventory. The hospital used RPA to eliminate paper records and store data digitally. In the process, it reduced unnecessary functions, integrated operational silos, and streamlined the supply chain. The processing cost of claims and billing decreased from $4 to $1 per claim4.
Where to apply RPA
Robotic process automation is a technology that automates high-volume, repeatable business processes by mimicking the human interactions that initially executed the strategies.
RPA is best used for mundane and repetitive tasks such as
logging into web apps, filling in forms, and pulling data from disparate
internal systems (see Figure 2). RPA bots are programmed to follow "if-this-then-that"
rules and therefore work well within processes that are already well understood
and where the data formats are structured and standardized. However, bots can
be used to structure data through their outputs further and can be a vital tool
in creating a standardized data architecture.
In the U.S., missed appointments cost approximately $150 billion annually in physician time and patient health5. RPA can help streamline appointment scheduling and monitor patients after hospital discharge. When patients schedule appointments, the coordinator at the hospital has to align the patient requirements with doctors' schedules and availability. RPA bots can automate this process; they can collect patient data, such as personal information and insurance details, and schedule appointments according to these criteria. They can even be used to send automated reminders or to update patients when positions change.
RPA can also be applied to finance functions. Revenue cycle inefficiencies cost hospitals and affect their profits. Claims management is time-consuming, and when done manually, it is error-prone. An average 350-bed hospital misses out on $22 million in revenue annually due to inefficiencies in charge capture, account "payables and receivables", and claims management6. RPA can settle accounts within hours and virtually eliminate human error.
RPA and the road to AI
RPA is foundational for digital transformation. By
implementing RPA, hospitals begin a journey of cleaning and standardizing their
data and processes in a way that supports more advanced digital capabilities
such as analytics, machine learning, artificial intelligence and cognitive
computing. Figure 3 describes the different prospective stage.