The High Costs of Delaying AI Adoption in Revenue Cycle Management

Costly Reasons for Delaying AI Adoption

The prominent role of AI in healthcare is hard to ignore. In recent years, significant advancements have been witnessed in the field of revenue cycle management (RCM), particularly in coding and billing. The emergence of new technologies incorporating natural language processing, deep learning, and other cutting-edge techniques has revolutionized the process, surpassing the limitations of rule-based robotic process automation used in previous generations.

To illustrate, let’s consider medical coding. Traditionally, healthcare providers relied on teams of certified medical coders who possessed the expertise to interpret clinical documentation and assign the appropriate medical codes for billing purposes. However, the scarcity of skilled medical coders has become a growing concern, with their struggle to keep up with evolving guidelines.

In stark contrast, leading AI vendors have achieved remarkable levels of automation, accurately coding 80% or more of a provider’s encounters. These achievements directly translate into substantial cost savings, decreased denials, enhanced revenue capture, faster cash flow, and a multitude of other benefits.

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Despite the evident advantages, many provider organizations have been slow to embrace AI in their revenue cycle operations, which is a costly mistake. It is crucial for healthcare executives to promptly adopt AI for three primary reasons, which I will now highlight.

Reason #1: Missing Out on Significant Cost Savings and Revenue

To understand the significance of adopting AI, let’s examine a hypothetical scenario involving a typical health system. This organization handles 2 million patient encounters annually, resulting in a net patient revenue of $1 billion. Assuming a 2% operating margin (considering the challenges posed by COVID), the system generates approximately $20 million in operating income.

Given this context, let’s perform a quick estimation to demonstrate how coding automation can genuinely influence profit margins by reducing costs, minimizing denials, and improving revenue capture.

Coding Labor Costs

Labor costs pose a recurring challenge in the revenue cycle, particularly when it comes to conventional coding methods. The manual approach of traditional coding creates a significant operational burden, involving the recruitment, training, and retention of an in-house team of medical coding professionals. Outsourcing to third-party vendors offers limited benefits, with slow turnaround times, coding inaccuracies, and potentially higher denial rates.

In contrast, automation brings forth compelling advantages. By leveraging state-of-the-art AI technology, health systems can achieve complete automation for over 80% of their encounter volumes. Unlike less-sophisticated computer-assisted coding tools, AI coding requires no human intervention and can result in cost savings of 40% or more. For instance, if the average cost to code for the health system is $3.50 per encounter, automation would save approximately $2.2 million per year by reducing the cost-to-code for the 80% of encounters that are automated (roughly 1.6 million).

Thus, solely considering the marginal cost of labor, the provider can experience an overall increase in system-wide margins of 11.2%.

Denials Reduction

Traditional coding methods often result in unfortunate consequences such as claims denials. Due to limited resources within in-house teams and vendors, coding accuracy often suffers, increasing the likelihood of payers denying or delaying claims. This, in turn, hampers the health system’s top-line revenue.

By implementing automated coding, tangible benefits can be realized through improved accuracy, thus reducing the risk of denied claims. Let’s consider a hypothetical scenario where our system currently experiences a 12% rate of denials. Assuming that 5% of those denials are preventable through coding improvements and that coding automation can reduce denials by 53% (as observed by some early adopters), the technology would generate approximately $2.5 million in additional revenue.

With the reduction in coding-related denials, we witness an additional 12.7% increase in the operating margin, resulting in a total increase of 23.9%.

Revenue Capture

Automated coding solutions also facilitate improved revenue capture in appropriate cases. While human coders may occasionally overlook procedures or exhibit excessive caution, automated coding systems can more accurately assign acuity or severity levels to encounters and comprehensively identify all reimbursable procedures within them. This directly translates into a positive impact on the top-line revenue.

Organizations that adopt coding automation often witness a 1-2% increase in relative value units (RVUs) per encounter. Applying this trend to our example, assuming an average of 2 RVUs per encounter, the health system could capture an additional 64,000 RVUs, resulting in approximately $2.1 million in incremental revenue using the 2023 CMS conversion factor of $33.06 per RVU. It’s important to note that the exact impact may vary based on factors such as the average number of RVUs per encounter and the payer mix within the health system.

This achievement leads to an additional 10.6% increase in the margin, bringing the total margin increase solely through coding automation to 34.5%.


To put it simply, the incorporation of AI into revenue cycle management (RCM) serves as a vital solution for health systems, offering substantial savings and revenue opportunities. Even with conservative assumptions, our hypothetical health system could experience an impressive 35% increase in operating margin, rising from 2.0% to approximately 2.7%. This would result in a significant bottom-line growth of around $6.9 million. While coding automation represents just the tip of the iceberg in terms of AI’s potential in RCM, it already delivers compelling financial benefits for providers.

In the face of reimbursement challenges and escalating costs, healthcare administrators cannot afford to overlook the opportunity cost of delaying AI adoption. Embracing automation for coding and other RCM functions becomes a crucial lever for healthcare executives to achieve their financial objectives while simultaneously alleviating the burden on their teams and enhancing experiences for clinicians, staff, and patients alike.

Reason #2: Kickstarting the AI Learning Curve

The cost savings associated with AI adoption are just the beginning of the benefits. Most AI tools exhibit improved effectiveness and efficiency over time, thanks to their exposure to a larger and more diverse set of data. This continuous learning capability is a fundamental advantage of AI and carries significant implications for the RCM function.

In the context of medical coding, AI tools gradually enhance their automation rates and expand their coverage across different specialties as they gain more experience. As the technology successfully automates a growing proportion of a provider’s patient encounters, it expands its capacity to support coding in a broader scope and scale. For instance, a health system may start with an 80% automation rate in one department but quickly progress to achieve 90% or higher automation while expanding to cover multiple specialties.

By embracing the AI learning curve early on, a health system can experience stronger and earlier results, ultimately freeing up capacity across various departments. This positions the health system to structure its revenue cycle organization more flexibly in the future, reducing labor risks and enabling better management of fluctuations in patient volumes and needs. On the other hand, organizations that adopt AI later will find themselves playing catch-up, missing out on these advantages.

Reason #3: Building Organizational Competency in AI

Without succumbing to excessive hype, it’s evident that AI is a permanent fixture, particularly in healthcare. Leaders and managers must take proactive steps to ready their organizations for this paradigm shift, starting with fostering familiarity and competence with AI tools.

As AI technologies continue to advance and become more prevalent, the ability to effectively use and manage AI in the workplace will become an essential skill. Similar to many other domains, adopting a “learning by doing” approach is an effective way to begin building this proficiency.

Therefore, healthcare organizations that embrace AI automation early on will reap the benefits of a workforce that is proficient in integrating and overseeing AI throughout their operations. Over time, this head start can even provide the organization with a competitive advantage in attracting and retaining top talent who are driven by staying at the forefront and achieving optimal outcomes. In an industry with an average annual turnover rate of 25%, providing staff with exposure to cutting-edge AI tools can significantly enhance recruitment and retention efforts.

In conclusion, AI will inevitably become a part of your organization’s landscape. So why not proactively embrace it and offer your staff the opportunity to develop the skills they will need to thrive in this new era?

The Road to AI Adoption

Despite the indisputable advantages of adopting AI in the revenue cycle, some healthcare leaders may still harbor reservations about moving forward. Two common concerns revolve around accuracy and staffing implications.

In the realm of healthcare, accuracy and reliability are of utmost importance. The question arises: Can we trust AI to avoid errors? In reality, the latest cutting-edge technologies for medical coding, trained on an extensive volume of encounters surpassing what humans can observe, offer superior accuracy and quality. Similar to existing coding teams, routine auditing processes can be implemented to monitor and ensure the quality of output from AI tools. By partnering with a reputable vendor and conducting a pilot project in a single department, healthcare leaders can test and validate the technology firsthand before scaling up. This prudent approach allows them to unlock the benefits of AI while developing confidence in its capabilities.

Furthermore, it is crucial to view AI as a tool that enhances human capabilities rather than replacing them. Health systems that embrace automation seize the opportunity to augment job roles, relieve staff of mundane tasks, and empower them to focus on more intricate and rewarding responsibilities. This, in turn, often leads to increased staff satisfaction and retention over time.

Given the remarkable advancements in AI technology, the compelling benefits and evidence of automated medical coding cannot be overlooked. Healthcare leaders, along with their teams, cannot afford to delay any longer. The time has come to seize the opportunities presented by AI and capitalize on its potential for transformation.


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