By Fathom |
Five Critical Comparisons to Evaluate AI in RCM

Practice Management

Five Critical Comparisons to Evaluate AI in RCM

Date Posted: Saturday, June 01, 2024


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As Artificial Intelligence (AI) picks up momentum in healthcare, leaders in Revenue Cycle Management (RCM) are often at the forefront of decision-making—wading through technical assessments and vendor evaluations in an effort to determine the best partner for their billing and coding needs.


Clearly, healthcare organizations of all stripes are catching on to AI's potential and are eager to harness the power of sophisticated and high-impact technologies to improve both clinical and administrative challenges. For example, applications of AI in RCM can streamline administrative tasks, boost coding accuracy, and accelerate billing processes, leading to faster reimbursements and improved margins for providers.


Although the right AI investment will yield substantial return on investment (ROI) and tangible benefits, well-meaning providers may understandably struggle to keep up with the proliferation of options. Any casual observer of industry news—or even most LinkedIn feeds—is sure to notice the inundation of new funding announcements, product releases, or high-profile partnerships from companies touting AI solutions in healthcare. However, not all solutions are created equal.


In this increasingly crowded marketplace, leaders need to weigh their options carefully and cut through the noise to make confident decisions for their organizations. And the stakes can be high. The health tech landscape is full of examples of companies that haven't lived up to their promises—such as the recent case of Olive AI, which named "fast-paced growth and lack of focus" as the cause of its collapse in 2023.


So, how can leaders select the right technologies and ensure a payoff from their AI investments?

Five Key Criteria


Before making any significant AI investment, billing and coding leaders must thoroughly assess their organization's needs and strategic objectives. Articulating clear goals and challenges upfront will make it easier to pinpoint areas where AI applications can offer the most significant impact and drive meaningful improvements. Other contextual factors to consider include organizational size, complexity, and existing IT infrastructure.


Once leaders understand their unique organizational needs, they should evaluate potential AI applications through a lens of five critical performance dimensions. This comprehensive framework helps to de-risk the buying process and to surface the strongest technologies for adoption in RCM.


1. Quality : One of the fundamental dimensions to evaluate AI solutions is quality—put simply, how well the AI performs at the intended task. The exact meaning of quality varies based on the use case at hand and may also encompass notions of reliability and consistency. For example, in medical coding and billing, accuracy of the AI's output is key to quality. Inaccurate ICD-10 coding can lead to an average loss of $1,877 per inpatient case, according to “The Financial Truth About ICD-10 Coding Accuracy: Two DRGs to Watch” (HFMA, 2017), so holding a high bar for quality is paramount.


Payors may reject claims when codes are inaccurate or incomplete, delaying reimbursement for providers and forcing staff to invest time and resources in investigating and appealing denials. AI-based autonomous coding from a high-quality vendor boosts accuracy and thus helps hospitals to minimize coding-related denials. By reducing the number of denied claims at the source, providers can avoid costly appeals, resubmissions, and write-offs that ultimately harm their margins.


2. Efficiency : As most would expect, AI technology ought to enhance efficiency by maximizing output per level of input, reducing labor requirements, and expediting turnaround times, among other potential efficiency impacts. But what does enhancing efficiency mean in practice, and what are the results to look for in RCM? Let's continue the example of autonomous coding.


AI coding brings immense processing power and data together, empowering organizations to handle increased coding volumes faster and more accurately. This directly reduces costs for revenue cycle teams. Moreover, the technology is adept at flagging documentation deficiencies early on, minimizing the need for time-consuming downstream corrections. And with AI coding, the headaches of adapting to new guidelines are a thing of the past. By seamlessly absorbing coding updates, AI tools eliminate the need for extensive staff retraining. Overall, by reducing costs and eliminating or accelerating time-intensive activities, AI solutions boost RCM efficiency.


3. ROI : When shopping for vendors, it's essential to scrutinize track record, business model, and pricing, as these are key to ROI realization. Doing thorough research and prioritizing vendor reputation and reliability are both critical to successful AI investments. A vendor's business model also plays an important—and often overlooked—role in ROI determination. For example, significant upfront fees in the partnership model will delay the provider's timeline to break even. In contrast, strong vendors offer ROI from day one by charging no implementation fees, enabling billing and coding leaders to benefit immediately from the cost savings involved.


While vendors often boast impressive ROI figures, it's prudent for leaders to validate these claims for their unique context. Any vendor worth its salt will agree to a proof of concept to demonstrate capabilities and ROI before the provider has to commit, so probe hard on try-before-you-buy options with AI tools. Take autonomous coding, for example. An effective way to build confidence and de-risk the investment is to run a proof of concept, comparing the AI technology's coding results to the results of your team today so that you can validate the quality and accuracy firsthand.

4. Scalability : Flexibility and scalability are essential product attributes of AI solutions in RCM. As most provider leaders have experienced firsthand, the pace of mergers and acquisitions (M&A) in healthcare is monumental—Chief Healthcare Executive reports in “Hospital Mergers Continue Upward Trend in 3 rd Quarter” (2023) that there were 53 hospital M&A deals by the end of the third quarter of 2023 alone. Most healthcare professionals have been part of an integration and experienced the complexity of conflicting systems and configurations across facilities.

When researching AI vendors, look for a long-term solution that grows with the evolution of your technology. Providers should ask: Will this AI solution allow my company to stay flexible while our environment changes? Does the AI have the capacity to mold to my organization's requirements without compromising quality? For instance, when evaluating AI solutions in RCM, the underlying cloud-computing configuration and degree of customizability are important aspects of scalability and flexibility.


5. Compliance : Healthcare regulations and coding guidelines are constantly in flux, and staying on top of them is critical for RCM operations. For example, last year, the 2023 E/M coding changes posed a significant hurdle for many emergency departments, forcing coding teams to take time to learn and validate the new leveling approach. To minimize these disruptions, AI coding can implement regulatory, payor, and other guideline updates quickly to ensure organizations stay in compliance. In simple terms, the process is not unlike updating software on a laptop.


AI also helps to maintain compliant documentation. For example, if a diagnosis doesn't have sufficient accompanying documentation, AI can alert clinical teams to manage the deficiency. By identifying and rectifying errors early on, AI helps healthcare organizations stay compliant with regulatory requirements and standards—preventing a deluge of negative consequences such as denied claims and audits.


How AI Supports Provider Teams


Beyond the direct operational and financial impacts associated with the five performance criteria above, AI solutions such as autonomous coding can also offer significant relief and satisfaction to staff. Burnout in healthcare is chronically high, with more than 45% of staff reporting uncomfortable levels of stress or poor mental health, according to the CDC's report, “Vital Signs: Health Worker-Perceived Working Conditions and Symptoms of Poor Mental Health” (2023). At the same time, persistent staffing challenges, such as a 30% shortage of medical coders, according to the American Medical Association in “Addressing Another Health Care Shortage: Medical Coders,” contribute to bottlenecks and backlogs that harm morale. Leaders can improve work-life balance, heighten productivity, and increase satisfaction for physicians and staff alike by relieving administrative burdens through autonomous coding.


AI Adoption and Beyond

All told, the adoption of AI in RCM has the power to transform operations and drive positive financial outcomes. The right AI medical coding solution, as an initial focus for investment in the administrative domain, can offer myriad benefits to healthcare organizations, from enhancing accuracy and efficiency to improving staff bandwidth and boosting margins.


Amid an increasingly crowded and noisy marketplace of solutions, healthcare leaders can make informed and confident decisions for AI applications by assessing the five key criteria—quality, efficiency, ROI, scalability, and compliance. AI is reshaping healthcare, and finding the right vendor allows organizations to lead the pack and secure their financial health.


Austin Ward is Head of Growth at  Fathom,  the leader in autonomous medical coding. He oversees the company's go-to-market efforts and client analytics. He brings broad experience in health systems, technology, and data science and has worked at BCG, the Bill & Melinda Gates Foundation, and in venture capital. He holds an MBA from Stanford University, an MPA from Harvard University, and BAs from the University of Chicago.





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