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(HealthNewsDigest.com) – In the year 2000, there were about 35.6 million Americans over the age of 65. By 2030, that number is expected to more than double to 71.5 million. What this means for healthcare providers—and even more so for payers—is that a game-changing surge in insurance claims is on its way. Not overnight, but the avalanche nonetheless is coming.
Because the number of claims will never be lower than it is today, now’s the time to be proactive. Healthcare industry organizations on both sides of the payment equation need to realize that the cost of relying on antiquated, manual-based processing systems is simply exceeding the return.
The most ubiquitous problem in business operations is human error—sometimes euphemistically referred to as business process variance. Variance in manufacturing permits accuracy to fall within a specific range or tolerance. However, in business processes that are absolute, such as billing or accounting, variance is simply error. To decrease those errors and improve processing consistency and accuracy, the only viable alternative is the use of claims processing technology.
Yet there remains a problem. Advancements in existing technologies have hit a ceiling with regard to the level of performance and quality they can deliver. In this era of rapidly increasing claims loads, newer technology innovations such as robotic automation of payment and related services have the capability to augment performance, handle complexity and shore up quality—not in small increments, but by orders of magnitude.
Greater Savings, Efficiency
Robotic process automation solutions cut costs by reducing manual labor expense, improving quality (decreasing variance), and improving performance.
The unit cost of an automated process is typically 50% less than with manual labor—and it’s not uncommon to see much higher savings. Furthermore, the elimination of manual work has a significant positive ripple effect because many of the issues associated with claims processing find their roots in manual processing. These issues include everything from simple keystroke errors to more complex payment mistakes.
In healthcare, the manual cost of processing a hospital claim is about five dollars, whereas the invoiced amount of a hospital claim is several thousand dollars. Therefore, the cost of a single payment error is greater than a month of labor. Additionally, manual payment achieves 85% accuracy in the healthcare industry. Health insurers employ armies of auditors to improve this statistic, but this only adds to the labor investment. By comparison, automation robots typically achieve greater than 97% accuracy—with no auditing.
Cloud-based robotics (CBR), also known as virtual robotic automation (VRM), is the newest and most effective form of robotic process automation. CBR solutions operate identically to an outsourcing vendor, except that the work is being performed by a robot rather than a person. The systems connect over a virtual private network (VPN) and process work on a client’s systems in the same manner as a person would: through the user interface.
CBRs utilize technology similar to legacy “screen scraping” or “scripting” tools; however, they have far greater capabilities. For example, some CBRs use image recognition algorithms similar to those used in facial recognition technology to facilitate user interface automation. More importantly, they have business process logic and decision-making algorithms, such as neural networks, to automate sophisticated decision-making.
As the name implies, CBRs reside in a cloud environment. A cloud solution confers several obvious advantages including security, data storage, disaster recovery, and lower cost. The most important aspect of a cloud-based solution is on-demand processing capability. CBRs use virtualization, where each virtual machine is a new robot; this means they can take advantage of the vast numbers of VMs that can be created in a cloud environment compared to the limitations of a single server.
At present, CBRs are capable of replacing work performed by administrative staff and auditors. It will be a while before they are making management decisions or orchestrating changes to the actual business processes, but as data-analytics capabilities increase, CBRs will be given more latitude to amend processes and find better outcomes.
Implementation Challenges
The challenges of implementing a robotic automation system are dependent on the solution that a hospital or payer uses. Legacy systems required additional data-center augmentation, software licensing, setup, configuration, support and maintenance which are all significant challenges creating additional work such as loading data, integrating, interfacing reporting, and so on. In fact, these issues extended to most client-server software applications such as interfaces, mapping tools, and patient accounting.
CBRs take a big bite out of expensive IT outlays for new hardware, space, security and integration work. It also means that the solution itself is already up and running; therefore, the focus of the implementation staff can be exclusively on getting things working.
From a process perspective, it’s essential to think of implementation of any solution as an iterative process and not a go/no-go Apollo mission. Successful implementations are those that aim for production over perfections, which is another reason why the classic client/server application model is not longer a viable solution.
Common Misconceptions
There are several common misconceptions that seem to recur with automated claims processing. The first misconception is that improvement must come at once in the form of a large claims processing initiative. Time and time again, systems that implement continuous quality control realize much better results much faster. In fact, in the time it takes to implement a new large system and get to square one, a provider or payer taking an iterative approach will already have a solution in place and have realized significant savings.
Another misconception is that robotic claims processing automation is a complete solution. Technology is a tool to make manual processes more efficient, but it is not a panacea. A good technology solution paired with a bad process just makes the bad process more efficient. The enterprise should have a strong understanding of how to solve a problem first—and then the technology will achieve maximum improvement.
Lastly, few healthcare payers and providers understand how to properly test technology. To many, User Acceptance Testing (UAT) is synonymous with the first day of go-live. To some this may seem like reasonable thinking—but if you do adequate unit testing, such as regression testing and integration testing, the technology has been tested. The totality of this testing is not a replacement for UAT. UAT is a dress rehearsal of an entire process from beginning to end over a complete business cycle, demonstrating how it will be performed in production.
No aspect of healthcare reform has more bipartisan agreement than the reduction of healthcare administrative costs. Healthcare administration is an area of massive waste, accounting for $300 billion of the $1.3 trillion dollars spent annually in the industry as a whole. Considering the labor savings, payment integrity, performance and turn-around timeliness, automated claims processing is the reality that healthcare organizations must embrace now, in order to accommodate the skyrocketing processing needs of the future.
SAL NOVIN is CEO of Healthcare Productivity Automation, a healthcare-focused software development and consulting firm focused on saving money through the automation of manual processes. He can be reached at [email protected].
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