The intelligent revenue cycle: How and why AI is impacting claims

Read Time: 4 Min
Managing claims within the revenue cycle is a persistent challenge for the healthcare field. AI solutions aim to resolve the root cause of the problem.

Revenue cycle management has long been a thorn in the side of U.S. healthcare providers. Attempts to streamline medical claims management are often aimed at specific areas within the process. But with healthcare costs skyrocketing, margins decreasing, and providers scrambling to protect their margins, an overarching strategy is needed to simplify medical coding and receivables, improve cash flow, and reduce resource challenges and administrative costs.

Autonomous coding and the application of AI in revenue cycle management may offer such an opportunity. Applying these technologies could alleviate some of the most significant challenges facing the healthcare field and allow providers to scale operations without shrinking margins.

The state of healthcare revenue cycle management

The challenge today begins with most healthcare providers using manual processes to manage their revenue cycle. Manually applying medical codes to claims is time-consuming, subject to human error, and expensive. A study conducted by Harvard Medical School and Brigham and Women’s hospital researchers found resolving coding and billing practices for a single primary care physician could add $124,435 in prevention services and $86,082 in annual revenue to the bottom line. Coding errors result in additional claim denials, which take even more time and money to resolve. Due to this process, the timing or amount paid isn’t always predictable, and financing options on receivables are limited. Services could remain unpaid and on the provider’s balance sheet for 60, 90, 120 days, or even beyond that.

These longstanding issues aren’t new to healthcare providers, but they have been heightened due to COVID-19 and economic activity in the last few years. The pandemic put considerable strain on healthcare systems, and historic labor shortages and wage hikes shrunk the available pool of personnel or hiring candidates to support providers and their patients. Healthcare providers face shrinking margins as they struggle to hire and retain clinical and non-clinical staff. Additionally, by 2027, U.S. healthcare costs could be $590 billion higher than estimates made in 2019, which could further shrink provider margins.

Regulatory and compliance standards on price transparency can further complicate the issue. One example is the No Surprise Act, which went into effect January 1, 2022, and is intended to protect patients from surprise out-of-network (OON) bills. Under this new Act, providers need to identify OON claims, assign them codes quickly, and process the claims accurately to avoid fines. In a revenue cycle already experiencing slow turnaround times and high error rates, meeting regulatory and compliance requirements like this can be challenging when staff is stretched thin. Providers will likely need assistance meeting requirements, as evidenced by results from one Marwood Group Study: More than 60% of surveyed stakeholders cited the growing complexity of healthcare regulations as a driving force behind RCM solution demand§.

However, COVID-19 also accelerated digital transformation in healthcare operations. Providers ramped up telehealth capabilities, for example, to meet patient needs. Online payment capabilities also expanded. This push for digital solutions is now extending beyond immediate patient care to other areas out of necessity: 95% of providers surveyed by Becker’s Health Review are expected to purchase new technologies in 2023 to help manage their revenue cycle, patient intake, clinical systems, and telehealth. Specifically, they plan to invest in technology that can boost productivity or lower labor costs to address workforce challenges.

“Digital transformation is occurring across many industries to solve a variety of issues,” says Dan Storer, Senior Managing Director of Healthcare Banking at Huntington. “Healthcare is one of the last sectors to be able to innovate with emerging technology solutions because of its mature EMR adoption and industry-wide claims coding standards.”

As this technology adoption momentum accelerates and shifts toward administration and the back office, it could combat those pressures felt by providers due to economic activity. One Marwood Group survey found more than 70% of surveyed healthcare stakeholders believe outsourcing through RCM solutions will offer significant gains for their hospital and health system≠. Improving the accuracy and speed of medical claims can result in more favorable financing solutions for providers and allow them to evolve with the rest of the field.

Addressing revenue cycle challenges with technology

Healthcare providers committed to implementing technology have various options, from denial management solutions to payment automation. However, many of these solutions focus on specific payment or reconciliation steps – symptoms of the underlying problem – rather than solving the root cause. For example, denial management solutions might improve the process of identifying records likely to be denied, but those denials likely stem from errors in the process. The technology doesn’t address those, but instead seeks to fix the results of those errors.

Alleviating constraints in specific areas within the revenue cycle – medical coding and receivable financing – requires technology that tackles the root cause of the issues.

New artificial intelligence capabilities in healthcare stand to alleviate many downstream revenue cycle management issues. Investing in them may allow providers to scale claim processing to fit their workload, reduce insurance denials, and access more favorable receivable financing options.

How artificial intelligence can take on revenue cycle management

AI-powered technology targeting claims management can help automate medical coding and streamline claim creation and submission, denial management, payments, and receivable funding. When using this technology, providers can gain valuable data into healthcare claims and realize adjudication at discharge.

The revenue cycle challenge with claims begins with manual medical coding. AI-driven medical coding can augment existing coding teams to alleviate labor constraints and scale to meet an ever-changing workload. Rather than replacing people – a common concern about automation and AI – AI can work alongside existing code teams and alleviate the strain by taking on the tedious, error-prone work.

Adopting new technology can present its own challenges, especially when automated solutions require significant human intervention. With machine learning, a solution can only be as good as its human counterparts (which tend to have higher rates of errors). AI medical coding using natural language processing (NLP), on the other hand, allows the system to comprehend written physician notes and make judgments on its own outputs. Workers’ involvement would typically be in exception cases requiring creativity and critical thinking, areas people excel in, rather than repetitive coding.

“The use of AI to provide high fidelity into a medical claims’ ultimate cash value and payment date certainty is a game changer to investors and lenders interested in exploring supply chain finance market,” says Storer.

Healthcare providers investing in AI-driven solutions could lower administrative costs, improving working capital at a time when liquidity is being tightened. These solutions could also increase claims and payment behavior transparency and claim processing times.

How does AI stack up against current revenue cycle processes?

More accurate medical coding and fewer denials are significant wins in their own right. But consider the larger implications: A U.S. health system with six facilities and a team of 163 medical coders has been testing an AI medical coding solution since March 2022.

Comparing the team of coders and the solution side-by-side, here are the results:

  • The average number of days to code an encounter went from 18 days to seconds.
  • Coding accuracy was 61% for the full-time employee (FTE) coders, versus 92% for the AI solution.
  • The total cost for the FTE coders was reduced by 30-40%.
  • In 20 hours, the AI solution coded an amount that took the human medical teams one year to code.

This solution could assign medical billing codes to a claim within minutes of a patient’s discharge. That claim can be submitted, and the medical receivable can be purchased, same day, potentially reducing the waiting time on outstanding payments from 30-90 days down to just 1-2 days.

Imagine what that speed to payment could mean to your organization.

Improving the revenue cycle can benefit providers, and patients

Resolving the most significant revenue cycle management challenges may benefit patients as well as providers. Here are just a few ways these root-cause technologies can help both sides of the industry, making for better patient experiences and provider care.

Potential benefits to patients:

  • Increased medical coding accuracy resulting in fewer errors, claim denials, and unnecessary interactions with insurance and healthcare providers.
  • Solutions similar to Huntington ChoicePay® allow patients to be reimbursed more quickly when overcharged by hospitals or healthcare providers.
  • Reduces the fear of receiving an incorrect bill or being sent to collections over a disputed claim amount.

Potential benefits to providers:

  • Fewer funds locked in the revenue cycle allows for new financing options, including higher funding rates and off-balance sheet opportunities.
  • AI-driven risk engines predict payments and reduce uncertainty, potentially leading to financing opportunities for adjudication at discharge.
  • Improved cash flow by reducing time to payment from 30-90 days to as few as 1-2 days.
  • More accurate, predictable claim submissions and payments.
  • Automation technology could significantly reduce administrative costs, which accounts for nearly 25% of U.S. national health expenditures.

The future of healthcare technology

Healthcare is undergoing a seismic shift. As costs rise and labor shortages continue, healthcare providers will need to consider taking action to transform operations and meet patient needs. AI-powered technology solutions, such as AI medical coding and automated receivables funding, can help resolve significant problem areas and allow providers to scale operations without shrinking margins.

The Huntington Corporate Healthcare Banking group is committed to connecting healthcare organizations with the solutions and resources they need to thrive in this evolving landscape. Learn more about how Huntington meets the needs of its healthcare clients and their stakeholders by reaching out to your relationship manager.

Agarwal, Sumit D. MD, MPH, Sanjay Basu, MD, PhD, and Bruce E. Landon, MD, MBA, MSc. August 2022. “The Underuse of Medicare’s Prevention and Coordination Codes in Primary Care.” Annals of Internal Medicine. Accessed February 10, 2023.  

Fleron, Addie, Aneesh Krishna, Shubahm Singhal. 2022. “The Gathering Storm: The Uncertain Future of US Healthcare.” McKinsey & Company. McKinsey Global Publishing. Accessed February 10, 2023.  

§ Marwood Group Advisory, LLC. 2023. “Targeted Healthcare Information Technology Materials: Marwood Revenue Cycle Management Survey.” February 2023.


Becker’s Hospital Review. 2023. “Health Systems Boost Tech Spending to Tackle Cost, Labor Pressures.” Accessed February 10, 2023.  

McKinsey. 2021. “How to Reduce Administrative Spending in U.S. Healthcare.” Accessed February 10, 2023.

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