The healthcare landscape globally is undergoing a radical transformation fuelled by a powerful trio – technological innovations driven by artificial intelligence (AI), automation, and advanced data analysis. Healthcare leaders in the industry are recognising the potential of tech integration into their operations, with a majority believing it’s vital for revenue growth.
The buzz around revenue cycle management (RCM) automation is translating into action, with technology enablers diving deeper into its complexities. This is a result of the challenges faced by healthcare providers around tightening margins, escalating expenses and charity care.
The advanced automation tools are ushering in a new era of streamlined workflows for healthcare providers enabling data-driven decision-making to deliver maximum patient care and satisfaction. However, with the adoption of AI and newer tools, there is an emergence of some common misconceptions about automating healthcare RCM, where its primary role is only in boosting efficiency and better monitoring. Here are three myths surrounding automation in healthcare RCM.
Myth 1: Automation replaces humans in RCM
The integration of AI into RCM has sparked both excitement and apprehension. Fundamentally, AI is not here to replace humans; its purpose is to enhance our productivity and capabilities. This perspective is critical for understanding the true potential of AI in healthcare RCM. One key factor is identifying tasks that AI can automate and those that require human cognitive capabilities.
AI excels at automating repetitive tasks, freeing up valuable human expertise for complex decision-making and areas requiring empathy and critical thinking. The key lies in a collaborative approach, where AI handles the heavy lifting and humans provide strategic oversight. Effective automation in healthcare involves a “happy orchestration” between human and AI tasks, ensuring each task is handled by the best-suited entity. Digital assistants tirelessly manage routine tasks, continually improving their efficiency. Meanwhile, human experts leverage their judgment and experience for complex situations. A hybrid approach, where AI and humans collaborate, can only unlock the true potential, as AI alone cannot address the inherent complexities and needs of healthcare RCM.
Myth 2: Automation requires in-house development
The lack of built-in automation in existing systems requires healthcare providers to invest in custom solutions. The in-house RCM-specific expertise among organisation’s programmers often leads to suboptimal solutions. Programmers without deep RCM knowledge may create solutions that are inefficient or incomplete.
What’s needed is a seamless integration of automation tools like Robotic Process Automation (RPA) directly within EHRs and other platforms which can be enabled by outsourcing RCM. With a multi-disciplinary approach, healthcare providers can integrate outsourced RCM solutions from specialists with deep RCM understanding with automation expertise. Combining industry, technological, and platform expertise enables more comprehensive automation, leading to more effective and efficient outcomes. This not only streamlines the process but also avoids futile attempts at automating overly complex tasks.
Myth 3: All automation improves efficiency
Simply automating tasks doesn’t guarantee efficiency. Automation loses significant value unless it is unattended, or fully automating a process from end-to-end, without requiring human intervention. This end-to-end aspect is particularly difficult to implement in the revenue cycle, which involves huge amounts of unstructured data and reasoning-based tasks.
Easily implemented automations are typically process-oriented and can only automate tasks that follow a clear, linear progression from start to finish. This is why early efforts have fallen flat—many require significant human intervention. These attended automation can be difficult to use and even tend to backfire, causing more burdens in training, clean-up, and execution—especially when wielded by inexperienced teams.
The road ahead – Paradigm shift to cognitive automation
As opposed to simple rule-based automation, cognitive automation can execute tasks that require both reasoning and synthesis of unstructured data. Cognitive automation offers new opportunities to streamline processes that were previously dependent on human intervention. Some practical applications of cognitive automation include:
- Document interpretation: Generative AI can read and understand complex documents, such as procurement policies or clinical guidelines, and identify relevant information or exceptions. When provided with specific documents with medical necessity guidelines, cognitive automation reasons whether guidelines are being met. Consider the process of submitting clinical documents for prior authorisation. Traditionally, a human would review the documents to ensure all necessary details are included. With cognitive automation, an AI system could reason over the clinical data to determine if it meets the required criteria. This system could then use APIs to submit the documents directly, bypassing the need for manual review and speeding up the authorisation process.
- Eligibility verification: By processing explanations of benefits and other insurance verification documents, AI can determine eligibility and benefits, reducing the need for human review. Prior authorisation status is also accomplished, where cognitive automation goes out to payer websites to status the claim or prior authorisation request and bring that information back into the electronic health records (EHR).
- Denial management: AI can analyse denial reasons and their root causes, generate appeal letters, and predict the likelihood of claim approvals. These insights inform conversations with payers and directly impact time to payment.
In conclusion, while we do not see it ever fully replacing humans, the integration of cognitive automation in healthcare RCM will enhance productivity, reduce manual workloads, and improve accuracy. Automation can drive significant improvements in operational efficiency and revenue recovery.