Redefining the Future of Medical Billing through RPA

Reduce administrative burden. Improve accuracy. Accelerate revenue collection. They’re all top priorities for today’s healthcare organizations, and many leaders are increasingly turning toward robotic process automation (RPA) and artificial intelligence (AI) to help meet revenue cycle management strategic goals. In fact, most organizations expect AI to be widespread throughout revenue cycles within five years.

“The convergence of RPA and AI is redefining medical coding, billing, and the entire RCM, enabling providers to streamline operations, enhance compliance, and deliver better patient experiences,” says Karna Palanivelu, senior vice president of operations at Global Healthcare Resource. 

Understanding the role of RPA in healthcare RCM
"RPA in healthcare RCM automates rule-based, structured tasks, freeing staff from recurring activities and reducing human error," explains Palanivelu. To date, organizations primarily apply RPA to patient data entry and eligibility verification, charge entry, claim submission, payment posting, denial management, and accounts receivable follow up.

The impact of RPA in healthcare RCM is profound, he says. For example, Global Healthcare Resource has increased operational efficiency by up to 40%. In addition, it has improved collections by 30%, reduced denials by 88%, and improved the clean claim rate by 99%.

Why is RPA in healthcare RCM so helpful? Because so many of the tasks in RCM are repetitive, time-consuming, and prone to human error. As such, they are primed for RPA applications.

Knowing how AI complements RPA in healthcare RCM
However, it’s important to note that not all RCM tasks are straightforward, and they require more intelligence than what RPA provides. That’s where artificial intelligence in RCM can help. 

“AI brings intelligence and adaptability to unstructured data, decision-making, and predictive analytics,” says Palanivelu. “Technologies such as natural language processing (NLP), machine learning, and computer vision elevate automation to new heights.” 

For example, many healthcare organizations use artificial intelligence in RCM to predict claim denials, code medical records autonomously, answer patient queries, score patient payment probability, and detect payment anomalies (e.g., billing for services never rendered, submitting the same claim more than once, or billing for higher-level services than documented).

Leveraging the combined benefits of AI and RPA in healthcare RCM
When combined, RPA and AI provide the following benefits in RCM:

• Accuracy and compliance. With proper human oversight, AI and RPA in healthcare RCM reduce errors, ensure regulatory compliance, and adapt to new and evolving billing codes.
• Business continuity. AI and RPA in healthcare RCM fill gaps caused by workforce shortages, ensuring continuity in claims submission and billing.
• Data-driven insights. Real-time dashboards and predictive models optimize financial performance and forecast cashflow.
• Enhanced patient experience. AI and RPA in healthcare RCM enable staff to focus on patient care and improving patient satisfaction and loyalty.
• Improved patient accounts receivable. Predictive analytics and patient payment scoring improve collections and cashflow.
• Revenue integrity. AI and RPA in healthcare RCM promote proactive denial management and reduce revenue leakage. They also support value-based payments and payments associated with telehealth and remote care models.
  
  Addressing potential challenges
  

  With any technology, there are always challenges that organizations must overcome to be successful. For example, with AI and RPA in healthcare RCM, organizations must address data security challenges associated with handling sensitive patient data. Built-in compliance frameworks, encryption, and audit trails can help.

  Another challenge? Workforce limitations, specifically a lack of RPA and AI expertise. Palanivelu says the most successful organizations make a concerted effort to upskill staff, use a phased-in implementation, promote user-friendly tools, and focus on change management.

  A third challenge relates to data quality. In essence, AI and RPA in healthcare RCM are only as accurate as the data on which they rely. Poor quality data leads to poor quality workflows and outputs. Investing in data governance and quality controls commensurate with RPA and AI rollouts is so critical.

  Embracing the future of RPA and AI in RCM
  Today’s healthcare organizations have only begun to understand the power of AI and RPA in healthcare RCM. As technologies evolve, (and the industry continues its shift toward value-based, patient-centric care models) we’ll likely see the following advancements:

• Continued evolution of AI-augmented workforces. RCM teams will focus on exceptions and analytics, requiring upskilling and reimagination.

• End-to-end RCM automation. From patient registration to claim adjudication, bots and AI will guide processes, escalate exceptions, and resolve denials.

• Expanded use of intelligent chatbots. For example, patients will be able to access self-service portals, leverage automated billing support, and negotiate payment plans all via chat.

• Expansion into value-based care. Artificial intelligence in RCM will play a bigger role in tracking quality metrics, care gaps, and risk adjustment.

• NLP for unstructured data. For example, artificial intelligence in RCM will scan a denial letter and automatically draft a compliant appeal based on payer policy and clinical context.
  

Given these probable advancements, today’s healthcare leaders should ask themselves the following strategic questions:

1. Are we ready from a governance, compliance, and data security standpoint?

2. Can AI and RPA in healthcare RCM help us better manage value-based care and alternative payment models?
   

3. How can we use artificial intelligence in RCM to enhance patient financial engagement and self-pay collections?

4. How will automation affect our workforce and operational structure?

5. What measurable return on investment can we expect, and how quickly?

6. Which revenue cycle processes are best suited for RPA, AI, or both?
   
   “RPA and AI are foundational for intelligent, predictive, and agile RCM operations,” says Palanivelu. “The integration of these technologies is not just a trend but a necessity for future-ready healthcare organizations.

“RPA and AI are foundational for intelligent, predictive, and agile RCM operations,” says Palanivelu. “The integration of these technologies is not just a trend but a necessity for future-ready healthcare organizations.