Healthcare provider networks are under intense pressure to manage financial margins while investing in contactless patient experiences. With overall financial losses from COVID-19 expected to exceed $323 billion, $200 billion in administrative waste due to revenue cycle inefficiencies, and increasing pressure to meet digital consumerism demands, health systems must find ways to streamline processes, maximize revenue cycles, and cut costs. These industry trends drive organizations to invest heavily in automation solutions like artificial intelligence (AI) and robotic process automation (RPA) to relieve operational and financial pressures.
Providers frequently overlook how a multi-layered technology approach can increase value realization in a rush to invest in automation and digital solutions. They require intelligent automation (IA) platform that combines powerful AI technology levers like machine learning (ML), natural language processing (NLP), and optical character recognition (OCR) with RPA and workflow orchestration, allowing humans to work in harmony with these digital assets. This article will look at how this IA platform can be leveraged to deliver financial value strategically throughout the revenue cycle.
When used correctly, IA can assist health systems in generating new revenue streams by improving net revenue capture, delivering cost savings by automating time-consuming rules-based revenue cycle tasks, and generating more predictable reimbursements. Organizations must, however, assess how and where to apply technology to achieve these financial and operational outcomes.
Intelligent Automation and Unstructured Data
When dealing with unstructured data, such as an image file or a clinical chart, NLP and OCR technology must be used to pre-process or extract data. However, when dealing with large volumes of structured data, ML can be used immediately to assess trends and determine the best way to complete a transaction. RPA may be the best option for completing repetitive and routine revenue cycle transactions such as adjustments, insurance verifications, and payment postings because it employs digital workers to perform these actions accurately and quickly.
NLP and OCR technology in Healthcare
Organizations can use NLP and OCR technology to convert unstructured data from files commonly used in healthcare – medical records, scanned documents, and audio recordings – into structured, normalized data. OCR, for example, can convert PDF explanations of benefits (EOBs) into a data table, which RPA bots can then auto-post into patient records. Before adjunction, NLP can extract clinical terms from an EMR note and provide critical data elements to a machine learning model to assess the likelihood of medical necessity denials. NLP and OCR convert everyday documents into usable data for faster processing in these scenarios. The full IA platform is used to generate cost optimization and improve revenue capture across the enterprise of a health system. Because the technology can consume large volumes of data and then create learning algorithms that make consistent decisions on behalf of operators based on the task at hand, an IA delivery platform also provides health systems with better decision-making tools. For example, when analyzing data, ML can use historical claim reimbursement trends to predict potential write-offs and then use the integrated workflow platform to escalate high-priority items to operators or direct low-dollar write-offs to RPA to process. These learning algorithms can be applied to various scenarios within the revenue cycle to improve cost optimization and streamline revenue cycle operations.
Finally, while most revenue cycle processes can be fully automated, there are exceptions and use cases that necessitate human intervention. To create a natural orchestration and seamless hand-off between digital workers and humans, automation technology should be combined with an integrated workflow platform to determine whether a revenue cycle task should be automated or handled by humans.
Let’s look at a common workflow: correspondence management, to see how the IA platform works. Paper document processing is still widely used in healthcare, necessitating substantial resources from health systems. Receiving paper correspondence from banks, such as letters, checks, and EOBs, reviewing tens of thousands of daily files, and manually entering data into subsequent workflow solutions, for example, is required for issuing correct billing correspondence to patients.
Intelligent Automation to Reduce Revenue Leakage
With an IA delivery platform, this process can be automated by utilizing RPA to retrieve these documents, OCR and NLP technology to convert these documents into standard file formats, and then, once again, RPA to process and attach necessary documents to patients’ accounts in the accounting or indexing system. While these activities are taking place, the integrated workflow platform monitors them and flags any exceptions or high-risk materials that must be removed and handled by humans. This symbiotic platform develops standardized processes on which patients can rely, which can then be scaled. Reduce frustrating administrative errors, such as misplaced information, incorrect bills, or inefficient handoffs, which can prolong billing cycles, to improve patient experiences. Because manual errors are significantly reduced, and automation runs processes 24 hours a day, health systems typically see reduced revenue leakage and shorter cycle times with automated processes.
An IA platform can take on and standardize numerous revenue cycle challenges across a health system’s enterprise, removing many common administrative errors or interoperability issues. It provides leaders with greater visibility into daily operations, allowing them to be more proactive in finding opportunities to boost revenue streams (e.g., eliminating revenue leakage, increasing ease of scheduling/payment for patients, or maximizing patient volume) and continuously. The careful application of these technologies in a platform-based strategy enables provider organizations to increase revenue while decreasing costs, allowing them to focus on their primary mission of keeping their patient population healthy.
It’s time to abandon the “one technology fits all” mentality and look for solutions to strengthen and improve multiple revenue cycle workflows and tasks. Given the high cost of developing these digital capabilities, health systems require a partner who provides the right model and has made the necessary investments in cutting-edge IA research, fully-staffed teams with subject matter expertise, and data-rich analytics to foster continuous performance improvement. Health systems can produce successful results that achieve the intended benefits with an aligned partnership and IA platform.
Furthermore, there is no such thing as a one-size-fits-all digital strategy. Organizations require an IT partner who can tailor RPA implementation services to their specific needs while keeping the current situation and long-term goals in mind.
The Diligent Group is available for your company’s consultation, implementation, integration, maintenance, support, training, process automation, digitization, and robotic process automation. Consult with Diligent experts to find automation solutions that are right for your company.
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