As the realms of artificial intelligence (AI) and automation continue to evolve, their potential to redefine revenue cycle management (RCM) in healthcare is becoming increasingly apparent. However, despite these advancements, the revenue cycle best-practice methodology behind managing account inventory in health systems has remained largely static and inefficient. This creates significant revenue and cost risk, particularly in an era characterized by shifts in payer policies, workforce shortages, and loss of institutional knowledge, all of which exacerbate the challenge of overturning denials. To counter this risk, leading revenue cycle teams are adopting a more strategic approach to denials management—one that harnesses analytics in groundbreaking ways to bolster inventory workflow and optimize revenue. This article aims to provide insights into how actionable intelligence can be effectively utilized to stay ahead of denials trends, and strategies to manage and mitigate denied claims in real time.
The first step towards future-proofing denials management involves leveraging actionable intelligence. This entails a comprehensive utilization of data analytics to discern patterns in denial trends, thereby enabling health systems to proactively anticipate and address potential issues before they escalate. With the integration of AI, predictive analytics can be used to identify high-risk accounts, flagging them for early intervention, and thereby reducing the likelihood of denials.
Actionable intelligence refers to the application of data analysis to drive informed decisions in real-time or near-time. In the context of denials management, it involves the comprehensive utilization of data analytics to identify and understand patterns in incoming and outcome based denial trends. The purpose of this is to enable health systems to foresee potential denial issues before they escalate, offering the opportunity for preemptive intervention that can significantly impact revenue preservation. An efficient denials management process is not just about responding to denial instances but also about anticipating them. By harnessing the predictive capabilities of actionable intelligence, health systems can proactively manage potential denial triggers and reduce the likelihood of revenue disruption. The ability to predict and prevent denials, rather than just react to them, shifts the paradigm from a reactive to a proactive approach, resulting in more efficient workflows and optimized revenue outcomes.
Predictive analytics, powered by AI, form a crucial part of this strategy. These advanced tools delve into historical and real-time data to predict future outcomes, allowing health systems to identify high-risk accounts that are likely to result in denials. These accounts can then be flagged for early intervention, mitigating the risk of denial and ensuring timely and appropriate revenue capture.
Etyon’s AI’s machine learning algorithms can analyze large volumes of data to identify subtle patterns and correlations that might be missed by manual analysis. By incorporating AI into the denials management process, health systems can significantly improve their ability to predict denials. For instance, AI can identify patterns in payer behavior or spot inconsistencies in prior authorization or coding practices that are likely to result in denials. Once these patterns are identified, the health system can take proactive steps to address these issues, thereby reducing the likelihood of denials.
Case Study: Leveraging Actionable Intelligence for Improved Denials Management
A leading healthcare institution, was encountering escalating denial rates that were negatively impacting their revenue cycle. The traditional reactive approach to denials management was no longer sufficient, leading to extended delays, mounting backlogs, and lost revenue. Recognizing the need for change, they decided to shift their focus towards a proactive, predictive model, leveraging actionable intelligence to stay ahead of denials trends.
Problem
This IDN was grappling with an average denial rate of 10%, significantly higher than the industry standard of around 5-7%. Despite having a team dedicated to resolving these denials, the sheer volume was overwhelming, and many claims were left unresolved by the time they reached the 90-day mark, resulting in permanent loss of revenue.
Solution
The hospital decided to leverage actionable intelligence to gain a comprehensive understanding of their denial trends. They integrated artificial intelligence and predictive analytics into their revenue cycle management system, enabling them to identify patterns in denials and flag high-risk accounts for early intervention.
Implementation
The IDN partnered with Etyon specializing in AI and predictive analytics. They implemented Etyon’s community driven algorithm models to analyze vast amounts of data from different sources like Electronic Health Records (EHR), payer contracts, and billing systems. Etyon’s machine learning is designed to identify patterns and trends in denials, providing insights into common denial reasons, payer behavior, and even the impact of specific clinicians or departments on denial rates.
This actionable intelligence enabled the IDN to flag high-risk accounts early, providing the opportunity for preemptive intervention. For instance, if a particular coding pattern was linked to a higher denial rate, the system would identify it, and the claim could be reviewed and corrected before submission.
Outcome
Within a year of implementing this proactive approach, this IDN saw a significant reduction in their denial rate, bringing it down to 6%. The ability to anticipate and prevent denials led to more efficient workflows, reduced backlogs, and improved revenue capture. The actionable intelligence also provided insights into systemic issues contributing to denials, allowing them to make targeted improvements in their processes.
Conclusion
This IDN’s experience demonstrates the transformative potential of leveraging actionable intelligence in denials management. By integrating AI and predictive analytics into their revenue cycle management, they were able to shift from a reactive to a proactive approach, reducing their denial rates and optimizing revenue capture. This case study underscores the value of actionable intelligence in staying ahead of denial trends and future-proofing denials management.
Strategic management of denials involves a real-time or near real-time, proactive approach. By integrating AI and machine learning into their workflow, health systems can automate the process of identifying and categorizing denials, which allows for immediate action. Automated processes can be implemented to appeal denials swiftly, reducing time delays and minimizing revenue loss. Concurrently, AI can provide insights into the reasons behind denials, offering opportunities for systemic improvements to prevent future denials. Leaders in health systems must be prepared to adapt to these trends, taking proactive measures to integrate AI and machine learning into their workflows, adjusting to new care models, and ensuring data security and compliance with regulatory changes. With a strategic approach to denials management and a keen eye on the future, health systems can optimize their revenue cycle management, ensuring their financial viability in a rapidly evolving healthcare landscape.
Automation plays a crucial role in this proactive approach. With AI and machine learning, health systems can automate the process of identifying and categorizing denials. This not only reduces the manual labor required but also allows for faster and more accurate identification of denials. Once identified, automated processes can be implemented to appeal denials swiftly. This rapid response can significantly reduce time delays and minimize revenue loss. Additionally, automation can ensure that appeals are consistently filed within the payer's specified time frame, reducing the risk of denials due to late filing.
Beyond automating processes, AI offers the ability to provide deep insights into the reasons behind denials. By analyzing large volumes of data, machine learning algorithms can identify patterns and correlations that may not be apparent through manual analysis. These insights can highlight systemic issues contributing to denials, such as coding errors or discrepancies in documentation. Once these issues are identified, health systems can make targeted improvements to their processes to prevent future denials. This could involve training staff on correct coding practices, improving documentation processes, or refining workflows to ensure timely submission of claims. By addressing the root causes of denials, health systems can not only mitigate current denials but also prevent future ones, optimizing their revenue cycle management.
Case Study: Strategic Management and Mitigation of Denied Claims
A large southeastern IDN, a well-regarded healthcare institution, was experiencing a rising trend in claim denials, leading to significant revenue leakage. The management recognized the necessity of adopting a more strategic and proactive approach towards denials management. They decided to leverage artificial intelligence (AI) and machine learning in their workflow, to automate the process of identifying and categorizing denials, enabling immediate action, and facilitating systemic improvements to mitigate future denials.
Problem
This IDN was facing a denial rate of about 12%, significantly higher than the industry average. The process of manually identifying, categorizing, and appealing denied claims was time-consuming, leading to delays and loss of potential revenue. Further, the underlying causes behind the denials remained largely unaddressed, leading to repeat instances of denials.
Solution
The solution involved a strategic, proactive approach towards denials management, involving the integration of AI and machine learning into the hospital's workflow. By automating the process of identifying and categorizing denials, the hospital sought to reduce time delays, minimize revenue loss, and gain deeper insights into the reasons behind denials.
Implementation
This IDN collaborated with Etyon who specializes in AI and machine learning. They implemented an AI-powered system designed to automatically identify and categorize denial risk in near real-time. The system flagged denials as they occurred, allowing the denials management team to initiate appeal processes swiftly. This reduced the time taken to respond to denials and minimized revenue loss. Simultaneously, machine learning algorithms analyzed vast amounts of data to understand the underlying causes of the denials. The insights gained from this analysis offered opportunities for systemic improvements, such as refining authorization practices, enhancing registration processes, and optimizing workflows to prevent future denials.
Outcome
Within eight months of implementation, they saw a significant reduction in their denial rate, bringing it down to around 7%. The automated identification and categorization of denial risk streamlined the appeal process, reducing delays, and improving revenue capture. Moreover, the insights provided by the AI allowed the hospital to address the root causes of denials, leading to more effective prevention strategies. This systemic improvement not only reduced the volume of denied claims but also led to more accurate and efficient billing practices, further optimizing the revenue cycle.
Conclusion
This IDN’s experience illustrates the transformative power of strategic, near real-time management of denials. Through the integration of AI and machine learning into their workflow, they were able to automate processes, reduce time delays, and gain valuable insights that facilitated systemic improvements. This case study underscores the vital role of technological innovation in optimizing denials management and ensuring financial viability in the rapidly evolving landscape of healthcare.
In summary, the future of denials management in revenue cycle management hinges on the strategic integration of AI and analytics. The transformative power of these technologies, as illustrated in the case studies, optimizes and revolutionizes traditional workflows. By automating denial identification, accelerating the appeal process, and leveraging predictive analytics to proactively mitigate denials, health systems can drastically reduce revenue loss. Furthermore, the insights gained from AI-driven data analysis enable systemic improvements, addressing root causes and curbing future denials. These advancements signal a shift from reactive to proactive management, equipping health systems to stay ahead of denial trends and ensure financial viability in an ever-evolving healthcare landscape. As healthcare continues to navigate workforce shortages, changing payer policies, and loss of institutional knowledge, embracing these technologies is not just an option—it's an imperative. Future-proofing denials management through AI and analytics is the key to sustainable revenue cycle management in the healthcare industry.
"We're now working the right accounts at the right times to where our timely filing write-offs reduction have drastically reduced and that's great."
AVP RCM
"When you combine amazing technology with folks that know revenue cycle challenges inside and out, you get tremendous results."
Lab Director
" I knew we had denial challenges, but could never uncover them as quickly as Etyon's solutions allows us to without all the effort."
Director Process Improvement