Home 5 Lab Industry Advisor 5 Essential 5 An Automated Assist in Revenue Cycle Management

An Automated Assist in Revenue Cycle Management

by | Jan 21, 2025 | Essential, Industry Buzz-lir, Inside the Lab Industry-lir, Lab Industry Advisor, Reimbursement-lca

How automation, artificial intelligence, and other emerging technologies are elevating the efficiency and output of labs’ revenue cycles

Automation is fast becoming embedded within the medical laboratory discipline, with artificial intelligence (AI), machine learning (ML), and other emerging technologies finding applications throughout all stages of the clinical laboratory testing workflow.1 However, the use of these technologies in diagnostic testing is only one part of the equation. Emerging technologies are increasingly being applied to the underlying administrative and operational functions that enable the clinical lab, and the wider health systems in which they operate, to keep delivering care to patients—including revenue cycle management (RCM). As these technologies continue to evolve, how are they addressing the challenges labs face in billing and reimbursement, and what do lab leaders need to know to effectively implement and harness the power of automation, especially solutions powered by emerging technologies, within their revenue cycles?

Integration on all sides of the cycle

The ubiquitous emergence of AI and ML has placed these technologies in a distinctive position within the current billing and reimbursement landscape. Their rapidly growing potential and integration into routine health care incited payers to begin to reimburse clinicians and health systems for use of devices powered by these technologies in 2020.2 This also sparked continued discussions regarding the most appropriate reimbursement mechanism for these tools when taking into account their scalability and applicability. However, these developments have also come as healthcare systems and hospitals are increasingly taking up these technologies and other forms of technological automation within their RCM workflows.

A 2023 AKASA and Healthcare Financial Management Association pulse survey indicated that 74 percent of healthcare organizations are using automation in parts of their RCM operations—nearly half of whom are using AI in some form—and 80 percent of respondents who weren’t using automation at the time of the survey stated it as a priority to complete by the end of 2025.3

“As a tool, AI streamlines billing by automating claims, coding, and appeals—for example, natural language processing (NLP) tools extract codes directly from physician notes,” says Raemarie Jimenez, chief product officer at AAPC. “As a healthcare tool, AI technologies like imaging analysis or predictive health algorithms are starting to require reimbursement. This dual role highlights AI’s growing importance in healthcare finance and delivery.” As we previously discussed, payers are also integrating these technologies into their claim review and denial processes, elevating denial rates. Such increased denial rates have spurred further use by labs looking to circumvent these added denials.

Automating efficiency in, and errors out

What makes automation and incorporation of technological advances, including AI and ML, into RCM workflows so appealing to providers, including clinical labs? Similar to its application within other aspects of the clinical lab’s functions,4 automation and implementing emerging technologies—when applied effectively—can optimize and streamline RCM workflows and reduce administrative burden. It can also catch errors that may go unnoticed and potentially cause problems that impact the delivery of services, and the care patients receive later, if carried out manually.

“Billing systems often struggle with manual processes, data silos, and lack of interoperability,” Jimenez explains. “These challenges lead to inefficiencies and errors that automation and emerging technologies can address. For instance, automated preauthorization tools eliminate delays in patient care and reimbursements.”

Jimenez also highlights the ability of emerging technologies like robotic process automation (RPA)—an umbrella term for tools, which are typically software-based, that are designed to quickly and accurately mimic repetitive, routine, rule-based human tasks, such as data entry, simple calculations, moving files and folders, and completing forms—which may help to standardize a lab’s RCM processes.5 When used with other advancements such as NLP and optical character recognition, RPA can also extract information from scanned physical documents, images, and other files for further processing. This technology has already been successfully integrated into the revenue cycles of organizations in several other sectors, including the financial, manufacturing, and telecommunications industries.6 For healthcare organizations, including clinical labs, RPA offers benefits in claims processing and billing through increased operational efficiency and enhanced accuracy and compliance that lead to improved revenue maximization and reductions in the time and cost caused by claim resubmissions.

The expanding use of AI in RCM automation—which as Jimenez explains, can further streamline revenue cycle operations by improving claim accuracy and reducing manual workloads—is likely a result of its versatility. “We have seen many advances in AI and ML as well as automation in RCM,” she says. “The technology has many uses, including ambient scribing, authorization, denial management and appeals, medical coding and audits, payment posting, analytics for denials, and clinical documentation integrity.”

Implementing automation within your lab

As technology continues to develop, so will its use cases in clinical lab revenue cycles. “In the next decade, technology will make RCM more proactive, with AI predicting patient payment behaviors and detecting fraud,” forecasts Jimenez. “Interoperability improvements will allow seamless data sharing, enhancing patient care and financial accuracy.” How can lab managers and administrators effectively incorporate and use automation within their RCM workflows as this happens? Jimenez’s first piece of advice is to keep up to date with developments in the area—and to make sure that your RCM systems can do so too: “Providers can stay ahead by adopting scalable systems, staying informed regarding regulatory changes, and fostering a culture of adaptability.”

She also cautions against implementing and using automated processes that aren’t supervised by operators sufficiently trained in their use. “One major pitfall to steer clear of is over-reliance on technology without sufficient oversight,” she warns. “Automated systems can amplify errors if not properly monitored. Providers should also avoid implementing tools that don’t integrate well with their existing systems. To mitigate these risks, providers should invest in thorough training, pilot programs, and robust data governance practices. It is best practice to start with one use case for implementing technology to learn the process and understand all the costs involved for the initial setup and training that will be required.”

Finally, Jimenez emphasizes the fact that automation and emerging technologies aren’t meant to take over clinical laboratory RCM, but instead to provide a helping hand. “AI in medical billing is not a replacement for human expertise—it’s a powerful partner. It’s here to streamline tasks, improve accuracy, and free up your team to focus on what matters most: patient care. Embrace it thoughtfully, ensuring it works for your specific needs and goals.”


References:

    1. H Hou et al. Artificial intelligence in the clinical laboratory. Clin Chim Acta. 2024;559(1):119724. doi:10.1016/j.cca.2024.119724.

    1. RB Parikh, LA Helmchen. Paying for artificial intelligence in medicine. NPJ Digit Med. 2022;5(1):63. doi:10.1038/s41746-022-00609-6.

    1. AKASA. 74 Percent of Health Systems and Hospitals Have Automation in their Revenue Cycle. December 20, 2023. https://akasa.com/press/health-systems-automation-revenue-cycle/.

    1. CP Yeo, WY Ng. Automation and productivity in the clinical laboratory: experience of a tertiary healthcare facility. Singapore Med J. 2018;59(11):597–601. doi:10.11622/smedj.2018136.

    1. DA da Silva Costa et al. Robotic Process Automation (RPA) adoption: a systematic literature review. Eng Manag Prod Serv. 2022;14(2):1–12. doi:10.2478/emj-2022-0012.

    1. S Balaguru. Beyond Automation: Redefining Healthcare Revenue Cycles through RPA, NLP and Gen AI. Int J Sci Res. 2024;13(8):1570–1573. doi:10.21275/SR24826040259.

Subscribe to view Essential

Start a Free Trial for immediate access to this article