The transactional nature of revenue cycle management (RCM) coupled with a growing volume of tagged data are exacerbating complex healthcare revenue cycle management (RCM) processes – making it an area that is ripe for automation and the application of Artificial Intelligence (AI). Specifically, manual and redundant tasks across departments, such as patient access, coding, billing, and collections, are ripe for automation. AI also is appropriate for real-time analytics, prior authorization, and denial mitigation.
Further, AI can address some of RCM's most significant pain points -- increasing revenue capture, rising wages, higher denial rates, soaring inflation, and greater regulatory scrutiny faced by healthcare providers who face internal and external threats to profits and reinvestment in care delivery.
AI-driven, no-code platforms allow organizations to gain deeper insights into their operations and enable them to predict future outcomes (predictive ML) and apply data and model-based decisions to inform short- and long-term strategies (prescriptive ML), including developing cognitive digital workers to handle tedious tasks and implement the next best actions based on data, heuristics, and machine learning.
Automation is first
The first step in any automation/AI strategy is to understand the differences between the two, which is necessary to inform decisions regarding their role within the organization's RCM process. AI and automation are often used interchangeably, but there are many not so subtle differences.
AI is the collection of technologies that allow the machine to act at the human level of intelligence. This process requires learning from past experiences and self-correction to make decisions and reach conclusions. Automation is something that runs with little/no human interaction by leveraging specific patterns and rules to perform repetitive tasks.
While automation is about setting up "robots" to follow predefined rules, AI is about setting up "robots" to make their own decisions. Both run on data, but while automation manipulates that data on set rules, AI can interpret it and suggest the next best actions. Automation with AI can collect, manage, transfer, and understand data, and automated actions can be based on that understanding.
Automation is not possible or appropriate for all functions, but when augmented by internal teams can offer a hybrid approach that enables health systems to efficiently scale operations based on changing demands and volumes while freeing up valuable resources to focus on core competencies like solving complex problems, driving revenue, and strategic growth opportunities.
AI to maximize revenue
AI in RCM can provide opportunities to maximize revenue cycle performance by bringing interoperability to disparate legacy systems with data standardization and providing analytical insights into the performance of revenue cycle operations. AI can eliminate tedious, repetitive tasks, such as data entry, manipulation, and extraction.
Three phases of automation | |
| Basic, or Descriptive Robotic Process Automation (RPA) | Uses software robots or AI to automate tasks. RPA tools interpret, trigger responses, and communicate with other systems to perform repetitive tasks. RPA is appropriate for scheduling, claims management, payment processing, and workflow management in healthcare. |
| Enhanced Process Automation with Machine Learning (ML) (Predictive RPA) | Integrating ML into process automation can advance robots beyond rote process execution and allow them to take on tasks that traditionally require human decision-making. ML can help health plans scrub and structure provider data for use in automation, detect anomalies in claims and identify opportunities for process improvement within claims. |
| Cognitive Automation (Prescriptive RPA) | The most advanced phase of automation, Cognitive Automation, is based on ML and utilizes technologies like natural language processing and speech recognition. RPA provides cost-effective solutions for manual processes and helps employee satisfaction by giving them more time to focus on complex tasks and provide better patient care. |
Automation reduces the pain of evolving coding requirements
With continuously changing coding requirements, stringent documentation demands, and data segmentation across multiple disparate software systems, the processes required for modern revenue cycle management have never been more complex.
For many organizations, upfront costs and gaining executive buy-in are the initial barriers to deciding to implement AI and automation in RCM. Automation shows rapid ROI, strengthening the business case for upfront investment and easing hesitations. An experienced RCM vendor can offer realistic expectations about what can and cannot be automated and assist in prioritizing specific areas of operations based on the unique needs of the healthcare system or facility.
Automation in the real world
A 99-bed, not-for-profit acute care facility in central New York implemented a customizable automation solution to address challenges in coding workflow, manual worklists, coders/CDI traversing multiple applications to capture correct diagnosis, case assignments, etc. In addressing the many coding workflow and code assignment challenges, the hospital improved coder efficiency by nearly 40%. As a result, discharged but not final coded (DNFC) days decreased by 50%, and its Case Mix Index (CMI) increased by 4.59%, for $592,742 in optimized reimbursement. Overall, using CAC Software improved the revenue cycle process with a bottom-line impact of $1.03 million.
Similarly, a 470-bed healthcare facility implementing automation to its revenue cycle decreased DNFC days from 7.6 days to 4.7 days (38% drop), enhancing cash flow by $2,270,356. Their rejected claims decreased by approximately 30%, with complex denials reducing by ~13%.
Automation will continue transforming healthcare
AI and automation will continue to transform healthcare as more outcomes are reported from organizations taking leadership roles in its deployment. We are already seeing a surging demand for RPA, which will fuel a more rapid uptake in evolving technologies that offer a way to improve care and the bottom line.
There is more working against healthcare organizations than ever before. However, this is not a time to hunker down or retreat – providers must be bold in facing these financial threats. Automation will not solve all healthcare problems, but it does drive efficiency and can help overcome financial threats.
Author
