Revenue Cycle Management (RCM) is a critical aspect of any organization’s financial health, particularly in industries like healthcare where it involves the complex process of managing claims, payments, and revenue generation. These multi-stem processes involving eligibility checks, claims processing, payment collection and more are all increasingly susceptible to errors and inefficiencies.
With the advent of Artificial Intelligence, RCM has undergone a significant transformation. Advanced analytics, predictive modelling, and automation capabilities have made AI a cornerstone in streamlining revenue cycle processes. Let’s delve into this riveting topic and explore the potential of AI in revolutionising RCM…
The Challenge in Healthcare Revenue Management
Traditional Revenue Cycle Management processes encounter several challenges that hinder efficiency and effectiveness. Managing the tasks manually leads to a high risk of errors and delays in processing claims and payments. Denial rates for hospitals have been on the rise over the past five years, increasing more than 20%. It typically takes anywhere from 7 to 30 days for payment to be dispensed after claim adjudication, with claims taking between 30 to 90 minutes per claim, depending on the length of the medical documents.
Additionally, the complexity of healthcare regulations and payer requirements adds another layer of difficulty, requiring extensive knowledge and expertise to navigate successfully. Communication gaps between different departments and systems within an organization can further exacerbate these challenges, leading to disjointed workflows and inefficiencies. Moreover, the lack of real-time data visibility makes it difficult for organizations to identify revenue leakage or optimization opportunities promptly. Based on reports by CMS, Medicare FFS (fee-for-service) estimated $25.74 billion in improper payments including overpayments and fraudulent billings. According to the AHA’s Annual Survey of Hospitals, Medicare paid just 82 cents for every dollar spent by hospitals caring for Medicare patients in 2022. The same year, Medicare underpayments totalled $99.2 billion and 67% of hospitals had negative Medicare margins.
These challenges collectively contribute to revenue loss, increased operational costs, and reduced overall financial performance, highlighting the pressing need for innovative solutions like AI to modernize and optimize RCM processes.
AI Solutions in Revenue Cycle Management
By harnessing the power of AI, organizations can enhance efficiency, accuracy, and ultimately, maximize revenue potential. From identifying billing errors to predicting payment patterns, AI has become an indispensable tool in navigating the intricacies of RCM, driving financial success and operational excellence.
Felix’s Revenue Cycle Management (RCM) solution leverages AI and ML algorithms to extract data from medical bills and invoices to reduce processing time and improve accuracy. The tool helps healthcare providers automate medical claims processing, reducing errors and delays, and improving cash flow. By acting like an end-to-end hospital management system, the solution saves time and money and improves the bottom line while enhancing the overall patient experience
It has been shown that deploying digital solutions can help bring efficiency and speed to the process of claims reviews, bringing about a productivity improvement of 30–80%! This reduces the burden on administrative staff, ensures a higher accuracy rate, and prevents revenue leakage.
In the same vein, Felix’s Contract Ingestion solution streamlines the onboarding and integration of new contracts into existing systems. By achieving 100% coverage with high accuracy, this tool improves operational speed drastically, enabling organisations to scale their processing from 4 contracts per person per day to 1000! Such capabilities translate to massive cost efficiencies, potentially saving up to $7 million annually for healthcare providers.
It is obvious that ensuring accurate payments and maximizing revenue are paramount for organizations. The Felix PI – Payment Integrity & Risk Adjustment Solution is a comprehensive platform that optimizes payment accuracy and risk-adjustable revenues. This advanced tool enables healthcare organizations to achieve 95% accuracy with payment recommendations for pre-pay and post-pay claims, enhancing manual review efficiency. By analyzing vast volumes of data, automating the detection of discrepancies in Explanation of Benefits (EOBs), and accurately processing millions of claims daily, the software prevents payment errors, reduces fraud, and enhances risk adjustment strategies, resulting in significant cost savings and improved financial outcomes.
These comprehensive tools, powered by machine learning and advanced analytics have enabled healthcare providers to expand their capabilities and focus on strategic growth and patient care rather than back-office operations.
Conclusion
The future of healthcare revenue cycles lies in the synergy between human expertise and AI capabilities. The integration of AI technologies into physician revenue cycles offers a transformative path forward for healthcare organizations. At a time where efficiency and accuracy are paramount, AI-driven solutions transform the burdens of healthcare revenue management into opportunities for growth and enhanced patient service. These technologies are not just about keeping up with the current demands but are paving the way for a more sustainable and profitable future in healthcare administration.
Discover how FelixSolutions can empower your healthcare organization to optimize revenue cycles and enhance patient satisfaction. For more information, please write to us on hello@felixsolutions.ai