Published: Dec 12, 2022
Updated: Mar 25, 2024
Workflow Automation

Revenue Cycle Management Automation: How AI, NLP, OCR Multiply Its Benefits

Suzanne Delzio
Suzanne Delzio
8 minute read
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Today’s healthcare RCM automation is not your grandma’s automation. 

In other words, it’s not 2023’s RCM automation. 

By late 2023, 74 percent of revenue cycle leaders at US hospitals and health systems had automated some portion of their revenue cycle, a choice that won them lower cost-to-collect, increased productivity, and fewer claim denials and errors. 

Seeing more room for improvement, however, developers have begun combining automation (typically robotic process automation) with generative and other forms of artificial intelligence (AI) to multiply the work that automation can complete in the revenue cycle. Here, you can review how AI-powered automation will bring even more cost savings and workflow efficiencies to healthcare organizations this year. 

 What is RCM automation?

Revenue cycle automation (RCM) is the process of replacing the repetitive, rules-based, manual work that healthcare staff carries out with software that operates using robotic process automation, artificial intelligence (AI), and machine learning (ML). The synergy of these technologies ensures providers, patients, and payers can efficiently conduct comprehensive patient care. Automation helps healthcare organizations: 

  • streamline tedious, manual processes and workflows
  • reduce labor costs and time to complete tasks
  • avoid hiring additional staff
  • get insights on revenue cycle inefficiencies and opportunities for increasing revenue
  • improve the customer experience

Types of RCM automation

To be clear, the term “RCM automation” now encompasses robotic process automation (pre-programmed algorithms that automate tasks), often combined with traditional AI (which accesses big data to make decisions based on human-defined rules), and machine learning, a type of AI which uses historical data to build logical, predictive models. Artificial intelligence and machine learning are often used interchangeably, but machine learning is a subfield of artificial intelligence.

While much of the buzz today centers around the generative (as opposed to traditional) AI involved in ChatGPT, there are only a few cases in healthcare today where generative AI and automation work together to complete patient access and billing tasks. 

Traditional artificial intelligence encompasses even more technologies of use to today’s healthcare leaders. One type, natural language processing (NLP), automates tasks that once only staff could complete. It allows computers to understand human speech and extract information from unstructured data sources. For instance, it can pull a patient's account number upon voice request. Asking a system for data takes a fraction of the time required when endlessly scrolling through documents. 

Another type of traditional artificial intelligence working in conjunction with automation is optical character recognition (OCR). Given healthcare’s burden of complex paperwork, OCR has become a boon to document departments everywhere. OCR lets computers read the text in images or scanned documents. Among other functions, OCR can generate an invoice from a scanned medical record, eliminating the need for repetitive data entry.

Automation proves its usefulness

Enticed by McKinsey’s promise that technology stands to create between $350 billion and $410 billion in annual value by 2025, revenue cycle managers, vice presidents, and even physician group CEOs have been incorporating AI-powered automation into their workflows and organizations. While 26 percent of the healthcare leaders surveyed in the AKASA study mentioned above have not used automation in their revenue cycle, 80 percent of this slower-to-adopt group plan to within the next two years. 

They’ve probably taken notice that modernizing via the implementation of new technology has paid off for their peers. The 2023 National Association of Healthcare Revenue Integrity’s State of the Revenue Integrity Industry Survey has 73 percent of respondents claiming that automating revenue cycle processes had a positive impact on their revenue. Similarly, a Black Book survey involving 1,302 healthcare organization financial professionals, found that those who implemented revenue cycle automation software enjoyed a 27 percent reduction in cost-to-collect and an increase in net patient revenue of six percent.

Similarly, an Institute for Robotic Process Information and Artificial Intelligence study quoted in this KPMG analysis finds that robotic process automation can save healthcare organizations 25 to 50 percent. Best of all, in a recent SalesForce survey of automation users, 79% claim automation tools make them more productive. Eighty-nine percent even report higher job satisfaction after automation implementation. 

These findings are no surprise to industries like finance, retail, and air travel which leveraged automation to modernize over a decade ago. Today’s investors, shoppers, and travelers take conveniences like online stock trades, inventory updates, and smartphone check-in for granted. Healthcare is just now catching up. 

Robotic process automation evolves

The advent of “intelligent automation” 

While automation in healthcare all started with robotic process automation, the addition of new technologies outlined above has multiplied its capabilities. Today, most in the development space call the synergy of all of these technologies “intelligent automation.” 

Developers have been hard at work making RPA more powerful by combining it with artificial intelligence, natural language processing (NLP), optical character recognition (OCR), and predictive analytics. Developers synergize these technologies to mimic human cognition so that systems can handle more complex and variable processes automatically. Although it gets tasks completed, RPA does not involve advanced cognitive capabilities like learning or decision-making based on complex data analysis. When variation comes onto the RPA scene, an organization needs additional technologies to complete the work. 

Intelligent automation systems can analyze data, learn from it, and make decisions based on that analysis. These systems can adapt and improve over time without human intervention.  

Going forward, as you encounter the term “automation,” know that it has evolved past RPA to intelligent automation. 

Practical uses of intelligent automation in the healthcare revenue cycle

Enterprise leaders today are using intelligent automation to transform productivity, improve accuracy, reduce costs, and improve customer and employee experiences. These use cases reveal how.

  1. Software self-upgrades: By combining natural language processing with automation, some software vendors make it possible for revenue cycle managers to prompt their software system to upgrade itself. With NLP, users simply explain what they want their software to accomplish and the tool itself builds an automation that handles a certain task or workflow. In other words, rather than requesting an update from your software vendor, you can get it done yourself. Watch for mentions of this automation upgrade from RCM vendors. 
  2. Software breakdown repairs: Some vendors are also expanding AI’s capabilities to examine any faltering automation. These systems not only discover the root cause of issues but also correct them. 
  3.  Patient access automation: With OCR reading patient documents and referrals, NLP carrying out actions via voice command, automation sending prior authorizations and patient communications, and AI catching coding errors and predicting which patients will struggle with payments, intelligent automation eases patients through their care journey. It reduces wait times, facilitates referrals, and ensures a smooth start to the revenue cycle.
  4. Revenue optimization: By combining AI for predictive analytics with automated tasks, healthcare providers can proactively anticipate service demand, comprehend payer behavior, and customize financial policies. This proactive approach enables more strategic pricing, efficient contract management, and optimal resource allocation.
  5. Claims processing accuracy improvement:  Intelligent automation streamlines the entire lifecycle of claim management, spanning from creation and submission to resolving denials. This automation not only expedites reimbursement but also significantly reduces billing errors.  By streamlining payment processing, account reconciliation, and financial reporting, intelligent automation strengthens your financial management practices.

The integration of intelligent automation into healthcare operations presents a transformative opportunity for providers. These technological advancements promise to revolutionize the way healthcare organizations operate. 

Generative AI and automation: the new power couple

Above, we’ve covered how forms of traditional artificial intelligence combined with automation are already at work in healthcare revenue cycles (and really throughout our everyday lives.) 

The thrilling / terrifying generative AI boom behind movie-star image theft, college essay generation, and instantaneous software coding is also landing in healthcare organizations, although to a limited extent. 

At this time, Epic EHR system is using ChatGPT 4.0 for some health records processing. Telemedicine platform Doximity is rolling out a ChatGPT tool that can draft preauthorization and appeal letters. Both combine generative AI with automated processes to speed and improve patient access processes. 

Some RCM vendors are exploring how generative AI can personalize patient billing communications. Every patient has unique financial resources and payment responsibilities. If AI can parse out just which payment plans work for which types of patients, it not only saves staff significant time, but it also enriches the patient experience and enhances the likelihood of timely payments.

In the healthcare organization setting, New Orleans-based Ochsner Health has integrated ChatGPT to help clinicians answer emails. Chapel Hill’s UNC Health has created a generative AI-based internal chatbot (using Microsoft's Azure OpenAI Service) to respond to questions, provide real-time recommendations and directions to locations.

Will you adopt early or wait and see?

Generative AI is just now getting a toe-hold in healthcare. Rest assured, you have time to explore how this exciting new technology can be used in your revenue cycle. 

But don’t sit on the sidelines for too long. 

Combining AI with robotic process automation has the potential, according to McKinsey, to add up to 3.3 percentage points to labor productivity. Healthcare leaders are paying attention.  In a survey commissioned by UiPath and Bain on AI and automation, 70 percent of healthcare executives perceive AI as “very important” or “critical” to their growth. In addition to increasing productivity, these leaders believe synergizing AI and automation will improve their service offerings, and personalizing for improved patient satisfaction. As companies strive for the improvements AI promises, they will be using automation to achieve them. 

In 2024, as healthcare organizations look to outmaneuver each other by unleashing AI’s promise, it’s automation that will be doing the heavy lifting.  As Mike Gualtieri, VP and Principal Analyst, Forrester explains in the above survey, 

“If you’re looking to use AI, look at your business processes. Because that’s where the opportunities are.”

Automation and AI synergize to create new value for healthcare organizations. 

Some believe 2024 is the year that healthcare organizations turn AI’s promise into work with automation to improve revenue performance. 

Revenue cycle management workflows that can be automated

The first step in determining the optimal method for revenue cycle management automation is determining which of your workflows will benefit the most from support from automation and traditional and generative AI technologies. Breaking down each workflow into components will illustrate which tools are best suited for the job.

Eligibility verification and patient registration

Insurance eligibility verification and patient registration are the first steps in the healthcare revenue cycle. Automating these processes has a considerable return on investment (ROI) since it reduces claim denials and improves cash flow.  

The combination of automation and AI technologies is transforming revenue cycle management by streamlining the often complex and time-consuming process of verifying patient eligibility. Automation tools integrated with AI algorithms can swiftly analyze vast amounts of patient data from various sources, such as electronic health records (EHRs), insurance databases, and patient portals. By cross-referencing this information, AI algorithms can accurately determine patient eligibility status, including insurance coverage, deductible amounts, co-pays, and other relevant details. This ensures that healthcare providers have up-to-date and accurate information when verifying patient eligibility, reducing the risk of errors and claim denials.

Further, an AI-powered, automated system delivers eligibility and benefits verifications in real-time. It can instantly verify patient eligibility during appointment scheduling or registration processes. This real-time verification enables healthcare providers to promptly identify any eligibility issues and address them before services are rendered.

Manual processes, on the other hand, can be time-consuming and prone to delays. 

Finally, an automated eligibility solution often contains good faith and patient pay estimate capabilities. Since the passage of 2022’s No Surprises Act, providers must provide these financial responsibility statements to all self-pay and uninsured patients.  Automating good faith and other cost estimates not only provides patients with critical financial information they need, it sweeps in upfront payments and helps patients find the right payment plan, two revenue-improving tactics. As with eligibility verifications, good faith estimates can be time and labor-intensive to perform manually. With robust patient access software, however, you can easily compile and send good faith or patient pay estimates with little to no work on the part of staff.  

Submitting accurate claims and avoiding denials

AI-powered automation streamlines claim processing by automating repetitive tasks such as data entry, coding, and documentation. Unfortunately, errors plague the majority of medical claims. Natural language processing (NLP) algorithms can review clinical documentation and coding to ensure compliance with coding guidelines and reduce the risk of claim denials due to coding errors. Automated scripts can find these errors that are commonly overlooked. Getting claims filed correctly the first time helps you avoid the $25 per claim it can cost practices and the $181 it costs hospitals. 

 Machine learning algorithms can analyze historical claims data to identify patterns and predict the likelihood of claim denials. Correcting errors that lead to rejection before claims are submitted is the most cost-effective method of denial management. 

Revenue is saved, too, when the time involved in workflows diminishes. An automated system follows up on unpaid claims and payment posting in a fraction of the time it takes a staff member.  Healthcare organizations improve claims accuracy, shorten the reimbursement cycle, and improve cash flow when they turn claims over to machines. 

The AI algorithms involved in claims software also provide revenue optimization insights by analyzing vast amounts of data, including patient demographics, payer trends, and reimbursement patterns. Predictive analytics then uncovers opportunities for revenue enhancement, such as renegotiating payer contracts, optimizing pricing strategies, and identifying high-value services. 


Despite a widespread preference for online payment options, 70 percent of today’s consumers still receive medical bills in the mail. While that’s a reduction of just 10 percent from 2018, it’s still a surprising figure given RCM and EHR technology’s (portals!) recent advances. 

AI-driven automation solutions send reminders, notifications, and payment requests to patients via various communication channels such as email, SMS, and patient portals. By automating these tasks, healthcare organizations increase the efficiency of their collections efforts and improve cash flow. Their algorithms analyze patient data, payment history, and other factors to prioritize accounts for collection efforts. By assigning accounts with the highest likelihood of payment to staff first, healthcare organizations can optimize their collections strategies. 

It’s not just revenue at stake in collections, however. All healthcare organizations must pay close attention to compliance to avoid penalties and hits to their reputation. AI-powered collections software helps healthcare organizations comply with regulatory requirements such as HIPAA and the Fair Debt Collection Practices Act (FDCPA). By automatically flagging potential compliance issues, generating audit trails, and ensuring adherence to legal and ethical guidelines, collections software helps mitigate risks and avoid regulatory penalties 

Automating collections also lets you choose a personalized approach for each patient that's more likely to motivate them to comply. Patients who are happier with their care and experience settle their bills more quickly.

Claim underpayment detection and appeals

Because underpayments are widespread, finding and detecting them should be a top priority for revenue cycle management automation. 

Providers lose one to three percent of their net revenue when commercial payers don’t reimburse at their contracted rates, according to a study published in Becker’s Healthcare Review. Others put that figure as high as 11 percent. Our clients commonly report that this failure to meet contracted rates takes away five to seven percent of their revenue.

But because underpayments typically contain some reimbursement, they can be harder to detect than denials. With denials, the payer provides a notice that a denial has occurred and gives the reason. With underpayments, when the payment comes in, staff often just marks the account paid. Only the most alert staff, or underpayment software detects the discrepancies between actual payments and contracted rates. We helped a 30-location orthopedics practice recoup $10 million in underpayments. That influx helped them hire more physicians and fuel growth.  

Payer contract analysis and reporting

While 58 percent of providers review their payer contracts annually, they don’t often push back on payer terms and fees. Worse, 17 percent of payers never review their contracts, and 16 percent take a look only every two or three years. Can you imagine how out-of-date their fees become? Payers have grown accustomed to steamrolling providers under their large legal departments.

It doesn’t have to be this way. Last year, physician group TeamHealth won $10.8 million dollars from United Healthcare in an underpayments case. Currently, over 100 Alabama hospitals are now suing Blue Cross, claiming a $5 billion loss stemming from underpayments.  

AI-driven underpayments automation ingests, digitizes and parses your contracts so you compare your reimbursements to Medicare's current rates. It also makes it possible to analyze and compare contracts for your payers against reimbursement per CPT code. Conducting this analysis for 25 CPT codes per payer would be intensely laborious if you conducted it manually. Automation makes it practically effortless. With competitive intelligence, you can get the most out of payers.

Benefits of RCM automation software

Automating your revenue cycle management will bring many benefits to your practice, such as:

Reduce AR days

The average range for A/R days is 30 to 70 days. If you have accounts that are routinely over 50 days, it could indicate that your denials are too high. AI-driven automation software drops your denials so that your average days in A/R drop, too. 

Improve patient experience and loyalty

AI-driven RCM automation speeds and simplifies the patient journey starting at eligibility verification and prior authorizations carrying through to charge capture and in triggering online billing statements and reminders. Providing uninsured patients with accurate good faith estimates establishes trust and gives your patients a realistic expectation of what they'll have to pay.

Increase staff productivity

All across the United States, AI-driven automation has stepped in to get healthcare organization revenue cycle tasks started and completed, even in the face of unprecedented staff shortages.  As mentioned above, staff who get software support in menial, tedious tasks are happier and less stressed. Computer scripts perform some tasks much more accurately than humans. Finally, because staff prefers to perform duties that require the human touch, their satisfaction and likelihood of staying with the job improve when software provides an assist. 

Scale financial operations intelligently without having to rely on increasing headcount

RCM automation multiplies your staff capacity without you having to hire additional FTEs. As the federal government began enforcing 2022 No Surprises Act rules, one women’s health physician group with locations in five states, needed to provide good faith estimates to tens of thousands of their patients. To get this work done, they would need to either hire four to eight new revenue cycle staff or use automation. The additional staff would cost them between $177,000 to $344,000 in labor expenses (4 to 8 X full annual FTE costs of $43,000) each year. The good faith estimate software they adopted cost them just a fraction of that. Upon patient scheduling, this system compiles and sends good faith estimates to all self-pay and uninsured patients. 

Achieve compliance with price transparency laws

The No Surprises Act went into effect on January 1, 2022 and enforcement the federal government began investigating consumer complaints in early 2023. Software vendors are dedicated to including compliance functionality so that you can achieve compliance with price transparency laws easily.

Recover more revenue from insurance companies

By establishing accurate benefits eligibility, collecting more patient payments up front, optimizing your claims to avoid rejection and denial, detecting and curing underpayments, and appealing denials, you'll recover more revenue from payers that would otherwise be written off. When you're paying your staff to handle denials or underpayments, you have to consider the cost of recovering the payments versus the amount recovered. With an automated system, you don't have to worry about that.

Automate revenue cycle management with MD Clarity

Hospitals, health systems, and physician group leaders now realize that AI-powered automation is crucial to revenue generation, patient financial experience, and other vital aspects of operations. As claim volumes increase once again and many organizations face severe staffing shortages, economists expect the global medical automation market to grow at a compound annual growth rate (CAGR) of 9.04% from 2023 to 2030. 

MD Clarity’s automation has been helping physician groups and management services organizations unlock revenue currently tied up in aging A/R, denials, underpayments, and more. Our RevFind consolidates all payer contracts in a single location and then unleashes its analytics tools on it to compare every actual payment to payer contract terms. It alerts staff to any discrepancies. Pursuing underpayments can result in millions of dollars in cash recovered and improved margins. RevFind also makes comparing contracts simple, providing the data that empower you to negotiate with payers in a more informed and assertive manner. 

To address revenue opportunities on the front end, Clarity Flow automates eligibility verifications and generates accurate patient or good faith estimates. After drawing from payer contract and patient records data, it compiles the estimate and then sends a text or email to the patient delineating their financial responsibility. These communications increase the patient’s confidence in using your services and empower them to determine how they will pay. Patients can easily send in a deposit or prepay the entire estimated bill. Clarity Flow reduces confusion surrounding the payment experience and helps them pay more upfront. 

Schedule a demo to see how these products sweep in the revenue you’ve already earned. 

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