Understanding The Role of AI in Revenue Cycle Management?

Integral to healthcare services, Revenue Cycle Management involves handling both administrative and financial processes throughout the patient journey. From registration to coding, claims submission to billing, collections to compliance and reporting, all critical aspects form the revenue cycle, guarding the financial health of a medical services facility and offering superior patient care.

With the advent of artificial intelligence across various sectors, AI has become increasingly prominent in Revenue Cycle Management. Displaying a potential CAGR of nearly 15.2%, the RCM market is expected to reach $275 billion by 2029. Leveraging the power of advanced analytics and Machine Learning (ML), AI can streamline routine functions through automation and AI-driven communication, optimizing operational tasks and financial processes for greater efficiency and agility.

AI-driven revenue cycle management emphasizes combining human expertise with AI to drive informed decision-making and efficiency. As many as 90% finance analytics in healthcare will adopt AI by 2027, to automate processes, reveals a Gartner Market Guide.

Witnessing the Change: Top Use Cases of AI in RCM

AI use in the healthcare revenue cycle is permeating all areas of administrative and financial operations, improving the overall productivity and translating it into monetary gains for the sector. Some notable application areas for Artificial Intelligence in healthcare revenue cycle include:

• Automation

  Simplifying mundane and routine tasks, AI is streamlining aspects like coding, invoicing, and claims processing with ease. Besides enhanced efficiency in everyday operations, it also helps reduce manual errors by minimizing human intervention

• Revenue Optimization with Predictive Analytics

  By drawing detailed and real-time analysis of historical data (medical and non-medical), AI enhances the work queue optimization process, forecasts better claim outcomes, and recommends more strategic resource allocation, contributing to the overall revenue

• Improved Medical Coding

  Designed to save time and effort, automated AI tools code and translate critical documentation into medical codes with high accuracy and prevent coding errors, enabling more precise and swift outcomes

• Better Patient Engagement

  Access to precise billing estimates with real-time costs and transparent billing details allows healthcare providers to improve their customer satisfaction rate and remain compliant with payments

• Handling Errors and Denials

  Using AI in revenue cycle management, it is easy to predict potential denials, even before the submission, which helps save time, enabling faster resolutions and less revenue leakage

• Verification for Coverage and Eligibility

  With access to data, providers can easily verify insurance, ensuring minimal errors and discrepancies for patients

• Workflow Improvisation

  Automation with Artificial Intelligence in the healthcare revenue cycle allows enhanced operational efficiency. By prioritizing complex cases, it streamlines work, improving overall productivity.

Immersed in Impact: Benefits of AI for Healthcare Providers

Redefining the healthcare gamut, AI has evolved medical care services for both patients and providers. Driving efficiencies through operations, billings, and patient care, Artificial Intelligence helps in the healthcare revenue cycle to generate superior outcomes in the following ways:

• Better Diagnosis

  Quick and large volumes of data can be analyzed using AI tools, enabling more accurate and precise diagnosis and early disease detection for quick prevention measures

• Bottomline Reduction

  Automation enabled by AI reduces efforts and costs for providers while minimizing errors. Less human intervention takes down the operational costs and aids in saving time

• Improved Risk Prediction and Data Handling

  Leveraging big data, service providers can predict the risk of diseases, enabling proactive measures for prevention and better management of pandemics and epidemics

• Consistency in Administrative Tasks

  By automating routine tasks like scheduling appointments, data entry, maintaining medical records, and other similar activities, AI helps create time for healthcare professionals to focus on more strategic and relevant tasks.

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• Enhanced Patient Care

  By integrating technology with clinical workflows, reducing manual tasks, and enabling better utilization of resources, AI empowers providers to enhance the overall quality of patient care and satisfaction

• Optimized Workload

  Advanced technologies such as medical scribe and automated optimization of workflows help spare time for clinical staff, prevent high-pressure burnouts, and enable them to focus better on patients

• Access to Digital Care

  By enhancing ease of access through aspects like telemedicine, virtual consultations, and continuous monitoring using wearables, AI is benefiting patients with timely and accessible care at any time.

• Consistency in Administrative Tasks

  By automating routine tasks like scheduling appointments, data entry, maintaining medical records, and other similar activities, AI helps create time for healthcare professionals to focus on more strategic and relevant tasks.

Glancing through the Challenges While Implementing AI in Revenue Cycle Management

As AI marks a paradigm shift for the healthcare services sector, providers often encounter complex challenges while embracing AI in the healthcare revenue cycle, which disrupts and slows down the adoption. Organizations face the following key challenges:

• Inconsistency in Quality and Data Integration

  Existing legacy systems have limited interoperability, making it cumbersome to integrate systems like Electronic Health Records (EHR) and other billing platforms with AI-driven tools, often resulting in inadequate data

• Heavy Capital Investment

  To invest in new technology, redesigning the existing processes, and bridging the skill gap with training can be very costly for many healthcare firms, compelling them to slow down the process or limit their AI adoption

• Skill Gap

  AI deployment in revenue cycle management requires various teams to be skilled in operating and understanding the workings of AI frameworks and applications, which are complex and can be a time-consuming process to bring them up to the mark

• Resistance to Change Management

  Strategizing change is critical for providers opting to deploy AI across departments, as employees can resist the practice owing to fears of displacement or discomfort with using technology. A well-designed change management program that guides the employees at every step of the change is crucial for adoption

• Compliance Issues

  Providers need to remain compliant and abide by the changing rules and regulations around billing and other legalities while aiming to embrace AI in the healthcare revenue cycle seamlessly

• Human-AI collaboration

  While AI can substantially accelerate operational speed with automation, to achieve optimal results for AI adoption, providers need to combine automation with human oversight.

Helming Change with AI in Revenue Cycle Management: How WNS Drives Excellence with Healthcare Clients

Defined by a comprehensive approach that optimizes AI-driven platforms and solutions with industry expertise, WNS offers holistic RCM solutions for leading healthcare providers globally. From an end-to-end suite of services supported by a technology-enabled platform, RCMplify, to proprietary automation tools like WNS Skense, WNS has been delivering tangible cost reductions of up to 30% with data-driven and scalable solutions.

Learn more about how to transform your revenue cycle operations while staying compliant and profitable by connecting with our experts here.

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