Claim denials continue to be one of the most dreaded setbacks for healthcare providers, tormenting their revenue cycle and costing them a staggering $20 billion every year. With around $43.84 as the average cost of fighting a claim, the stakes keep getting higher. Shifting payer behavior, fewer hands on deck to deal with the rising patient and claim volume, and heavy reliance on manual claims management result in process inefficiencies and revenue leaks, further adding to their woes. However, here’s some good news: about 90% of the denials can be prevented and around 63% can be recovered on appeal. Artificial intelligence (AI) in denial management can be a game-changer for healthcare revenue cycle management (RCM) in this regard.
AI is fast becoming the most sought-after technological aid among healthcare providers to boost operational efficiency through evidence-based process improvements. It has successfully helped digitally transform practices end-to-end, and healthcare denial management isn’t untouched by its impact.
Why a strategic approach to AI in denial management matters!
AI-powered RCM solutions are taking the healthcare industry by storm, with their proactive approach to managing claims and payer denials. Leveraging machine learning (ML) in denial management to learn from data patterns, AI helps analyze large data volumes to offer evidence-based resolution to potential denials while streamlining claim processing. Besides, it supports the automation of revenue cycle operations to reduce the likelihood of potential human errors, thus saving the staff their valuable time and ensuring accurate, timely claims submission for speedy payments. Owing to its limitless capabilities and numerous use cases in RCM, the technology has proved to be a holy grail for the early detection of potential denials and the management of recurring denials caused by human or systemic errors.
However, leveraging its full potential for effective RCM requires healthcare providers to have a strategic approach that is a perfect harmony of data, platform, and skilled workforce. This synergy allows them to make the best use of AI to beat their financial goals while working toward operational improvements for better patient experience.
The synergy that powers AI in denial management
Data: Healthcare providers need to have a robust, secured database, including claims data, clinical data, and updated payer and regulatory policies, to enable AI algorithms to effectively identify and predict denial patterns and support corrective actions for denial prevention and management.
Platform: A foolproof workflow management platform is critical to managing denials effectively. Think of this platform as the RCM engine propelling the application of AI in denial management through unified data access. This platform should support interoperability and cross-department collaboration, free of process siloes. This would allow access to critical, updated data and make necessary process improvements driven by actionable insights on key performance indicators (KPIs) for effective denial and accounts receivable (AR) management.
Specialists: Having a team of RCM experts by your side can help take your practice’s financial health to the next level with the effective application of AI in workflow management. These trained experts know well how to leverage revenue cycle AI to see and seal the gaps with timely intervention for the proactive management of and speedy resolution to denials for faster reimbursements and sustained cash flow.
Benefits of AI in denial management for streamlined RCM
The industry is witnessing the growing adoption of AI as part of providers’ digital transformation efforts for effective RCM. With AI facilitating predictive analytics, business intelligence, and process automation, providers can realize:
Streamlined claims filing: By automating revenue cycle processes leading to claims filing, AI can help reduce the scope of manual errors and expedite claims processing while reducing any legal or compliance risks. With the prompt capturing of relevant information from vast databases, AI can help parse information much faster for speedy and timely submissions.
Improved clean claim rate: By reducing manual errors during claims processing, AI helps improve the clean claim rate, thereby reducing payer denials and improving the costs and collections for healthcare providers.
Denial prevention and management: The ML capabilities of AI help in understanding historical denial patterns and flagging similar patterns for potential denials for corrective measures. This further helps in preventing such denials with timely intervention. Besides, AI helps in managing payer denials by identifying and addressing the root cause behind those denials.
Improved compliance and patient experience: The reduced rate of claim denials bears testimony to accurate, complaint claims submission as per payer and regulatory policies. This further helps in speedy payment clearance for providers and an improved experience for patients.
Data protection: AI, while optimizing claims workflows, also protects sensitive data from any kind of breach or cyberattack, thus ensuring operational continuity without any data risks.
How providers can outsmart denials with confidence
AI is set to become a healthcare RCM staple, and providers willing to adapt to this innovation are bound to benefit from it. With its high-speed data-parsing ability, AI can help providers analyze huge datasets and denial patterns to identify trends and potential denials, understand missing gaps and systemic errors causing recurring denials, and resolve them with timely intervention. This intelligence further helps improve process precision, thus reducing payer denials and improving cash flow.
For providers looking for robust healthcare denial management services, a strategic partnership with a tech-enabled RCM solutions provider like Jindal Healthcare can prove critical. With Jindal Healthcare’s proprietary AI platform, HealthX, which supports BI-driven intelligent dashboards for KPI tracking, a decision-tree model for effective resolution to denials, and a propensity-to-pay model for prioritizing worklists and accounts with the highest likelihood to get paid, providers can rest assured of robust denial management and accelerated payments while ensuring compliance and staying true to patient care.
