The Role of AI in Healthcare Revenue Cycle Management (RCM)

The Role of AI in Revenue Cycle Management

Artificial intelligence (AI) in revenue cycle management (RCM) is the use of computer systems and machine learning to streamline, automate, and enhance steps in the RCM workflow. AI can support or handle tasks that would otherwise be manual, time-consuming, and error-prone parts of managing the healthcare revenue cycle. Plus, AI can analyze data on a massive scale to identify and address opportunities to improve revenue capture and cash flow.

Several distinct AI technologies—including machine learning, optical character recognition (OCR), large language models (LLMs), and generative AI—can play a role in the revenue cycle management process. This guide dives into use cases for AI in RCM and how the right tool(s) can make the revenue management process more efficient and effective.

How is AI Used in Revenue Cycle Management?

Healthcare providers use AI in revenue cycle management to automate tasks, improve accuracy, increase efficiency, and enhance staff capabilities. AI can streamline or automate RCM tasks in areas like eligibility verification, patient support, claims processing, compliance auditing, and fraud detection. AI-enhanced data analytics can also support staff efforts to forecast revenue, predict payment patterns, and reduce claim denials.

AI is in the spotlight not only because of its recent technological advancements but also for its potential to address ongoing challenges in the healthcare industry: a shortage of skilled healthcare personnel, rising costs of care, declining reimbursement rates, evolving payment models, and the need to reduce operational costs.

The use of AI in revenue management is growing rapidly, and 8 out of 10 healthcare organizations are actively seeking and evaluating new RCM technology. The use of AI in RCM can streamline healthcare operations and unlock new opportunities for revenue growth in many other ways—but only with thoughtful adoption, implementation, and oversight.

Automation vs. AI in RCM

Revenue cycle automation is the use of technology to perform routine RCM tasks with reduced human intervention.

Automation is rule-based, task-oriented, and based on predefined workflows, making it a good fit for tasks like data entry and eligibility verification. This is a huge opportunity—perhaps a $200-360 billion savings in U.S. healthcare expenses—for providers to improve efficiency.

However, certain revenue management processes—like managing claim denials—are too complex to write automation rules for each situation.

AI goes beyond automation to learn, adapt, and make intelligent decisions based on data analyses. An AI system can handle more complex tasks by observing ongoing functions, learning from the data, performing functions without explicit scripting, and adapting its behavior based on new data.

While automation is a great way to improve efficiency, AI helps uncover actionable insights that improve efficiency and effectiveness. For example, automation can help reduce errors in data entry by running preset quality checks, while AI can predict which claims are at risk of rejection based on historical and real-time data. Both will help improve the accuracy of claims submissions, but AI-enhanced tools allow more proactive revenue management.

The Goals of AI in RCM

At a time when healthcare costs are rising and reimbursements are declining, healthcare operations need reliable ways to improve efficiency and accuracy. AI and automation can make a significant impact here.

Healthcare finance and A/R teams can use AI to support several key goals:

• Drive patient and payer payments: Improve follow-ups with insurers and patients, automate payment reminders, customize billing rules and workflows, and offer patient billing support.
• Improve overall cash flow: Decrease days in A/R, improve point of service (POS) collections, expedite reimbursements, prevent overlooked charges prior to claim submission.
• Improve revenue collection with data: Analyze historical data, predict and prevent barriers to RCM, and enable real-time revenue analysis across platforms and payment methods.
• Improve operational efficiency: Streamline insurance verification, review patient financial data at scale, and identify outliers faster.
• Reduce administrative staff burnout: Streamline billing and coding processes, reduce manual data entry, automate repetitive tasks, and free up time for more specialized work.
• Improve the patient experience: Simplify the patient billing experience, personalize patient communications, offer payment plans, and make billing support readily available.
• Improve accuracy and efficiency: Reduce errors and omissions, minimize overlooked charges, and flag claims that exceed preset thresholds or violate specific rules.
• Reduce fraud and improve compliance: Track and analyze data at scale, recognize payment patterns, identify markers of fraud, and track compliance KPIs in real-time.
• Improve oversight: Reduce the manual effort required to find potential issues, flag outliers for further investigation, and empower staff to monitor revenue cycle KPIs
• Improve oversight: Reduce the manual effort required to find potential issues, flag outliers for further investigation, and empower staff to monitor revenue cycle KPIs

AI has incredible potential to simplify administrative functions—something that could save U.S. healthcare providers an estimated $175 billion annually, roughly 18% of admin spending.

Examples of AI in Healthcare RCM

The AI applications for healthcare RCM touch every stage of the healthcare revenue cycle.

For example, consider AI pattern recognition throughout the RCM journey: Machine learning algorithms can analyze medical records, claims history, and billing data at scale to identify patterns. AI tools can process this data to predict outcomes, flag potential issues, and recommend data-based solutions.

This data analysis improves the providers’ ability to submit accurate claims, detect fraud, predict patient payments, forecast revenue, manage cash flow, and train and coach staff—distinct steps at different stages of the process, all of which involve other systems and data sources, too. But each one can benefit from the thoughtful and strategic implementation of AI-powered data analytics.

The most common uses for AI in RCM are eligibility/benefits verification and patient payment estimation. But providers expect to increase their use of AI in areas like prior authorization, payment amount/timing estimation, and denials management in the near future.

From pre-registration through post-payment, see how AI can support and enhance these steps in the RCM process:

AI for Patient Billing Accuracy

Errors in patient billing are frustrating for patients and costly for healthcare systems. AI can help providers:

• Reduce billing errors
• Improve and personalize the patient billing experience
• Reduce the number of claims denied due to billing errors
• Audit billing data in real-time and monitor billing KPIs
• Improve charge capture and streamline payment posting

Automated and integrated systems can pull data directly from electronic health records and populate the appropriate fields in billing and claim systems, reducing manual effort and the risk of human error. On a larger scale, providers can train algorithms to recognize patterns in billing errors and claim denials, note changes over time, and recommend solutions to improve accuracy.

AI-powered data analytics empowers staff to investigate trends in specific claim types, payers, or categories of patient services. With these insights, providers can focus their efforts on the biggest sources of revenue leakage and the areas where staff may need the most support.

AI for Medical Coding Efficiency

AI-powered coding systems can analyze clinical documentation, identifying the most appropriate codes from thousands of options, and even suggesting codes that might have been missed by human coders. Automated charge capture tools, NLP, and rule-based technologies help ensure no documented charges are overlooked.

When claims are ready for submission, AI tools can quickly review the data to find and resolve coding errors before they are submitted to payers. These AI-enhanced RCM processes help improve clean claim rate, days in A/R, and other key metrics.

Healthcare providers can use AI-powered insights and training tools to enhance coders’ skills. For example, AI training platforms can demonstrate correct coding procedures and compare correct vs. incorrect examples to onboard, train, and coach staff.

AI has shown promising results for more efficient and accurate coding. A case study by Healthcare Financial Management Association highlights a hospital that improved coder productivity by 40% and decreased discharged-not-final-billed cases by 50% by using AI to connect clinical documentation and coding systems. The resulting ROI was over $1 million, more than 10x their investment.

AI for Patient Payment Estimation

Providers can better predict patient payments with the use of AI tools trained on large quantities of data. For example, AI can identify patients who are most at risk of having difficulty paying and suggest some personalized outreach or payment arrangement.

When integrated with financial assistance technology, staff can see all financial aid that might be available to a patient, which improves the patient financial experience, as well as organizational revenue capture.

AI for Benefits Verification and Prior Authorization

AI-enhanced tools help ensure patient information is accurate and up-to-date, including patient details, communication preferences, and payment methods. They can also assist with verifying insurance coverage, policy status, and coverage terms, helping to avoid costly delays and errors later in the RCM workflow.

Providers can use AI tools to streamline the patient registration process by notifying patients to update their insurance details when coverage is set to expire or change. AI-powered RCM tools can also verify deductible amounts, copays, and remaining balances—and make them available in staff views and patient portals—so all parties are well-informed.

AI systems can also improve the prior authorization process by reviewing patient data and medical records, cross-checking with payer requirements, and verifying prior authorization details with fewer phone calls and faxes.

AI for Patient Billing Support

Generative AI allows healthcare providers to offer 24/7 patient billing support with reasonable overhead costs. These interactive tools are trained on billing and RCM topics, payer requirements, and provider SOPs to offer real-time support for simple billing questions at the patient’s convenience. Chatbots and other self-service support options can extend the hours of patient billing support and enable staff to focus on more complex tasks.

AI-enhanced RCM systems can also create and create individualized payment plans for patients—without the massive manual effort required to do so manually. In some cases, AI tools can identify candidates for specific financial arrangements, like a deposit system for a patient with an upcoming procedure with a high out-of-pocket cost. This personalized outreach reduces financial strain for patients and improves the predictability of cash flow.

AI for Personalized Patient Communications

With smart communication tools, health systems can improve patient communications without increasing the burden on staff. AI can assist with personalized and accurate communication on several topics:

• Reminders: Send personalized reminders for upcoming appointments, payment due dates, current balances, and other updates.
• Cost estimates: Generate accurate upfront estimates of treatment cost, accounting for copays, coinsurance, deductibles, financial aid, payment plans, and other factors.
• Patient education: Send personalized financial aid resources and educational materials that increase financial literacy and open up two-way dialogue about the financial side of healthcare.
• Customized outreach: Identify patients who may need additional support, such as patients with high-cost treatments scheduled after their deductible resets, and recommend personalized outreach.

This level of communication improves patient education and financial transparency, as well as setting more accurate expectations about treatment costs. This helps set appropriate expectations for patients and ease the burden on staff, especially during deductible season.

AI for Claims Processing

AI can assist with claims processing by submitting straightforward claims, tracking claim progress, identifying mistakes, detecting fraud, and predicting claim approval.

Healthcare providers can use OCR to convert handwritten and printed text into digital text and convert claim payments into an industry-standard 835 file to save staff time.

Automated tools can also submit straightforward claims to payers when they meet certain conditions, as well as generate updates on claim status for staff review and follow–up. This saves time on straightforward claims submissions and recurring reporting.

Providers can use AI to predict the likelihood of a claim being denied based on historical data and patterns, allowing for preemptive corrections and reducing denial rates. AI-powered dashboards help leaders track denials by type or payer, providing useful insights to reduce common denials

AI for RCM Operations

In addition to supporting the RCM workflow, AI can assist with Finance and Accounts Receivable (A/R) team operations. Leaders can use AI to:

• Reduce billing errors
• Improve and personalize the patient billing experience
• Reduce the number of claims denied due to billing errors
• Audit billing data in real-time and monitor billing KPIs

Leaders can use AI technologies to assist with the initial learning curve and ongoing development of workers. For example, a 2023 report found that a generative AI-based conversational assistant in a call center improved productivity by 34% among novice and low-skilled workers (and 14% on average). The right AI systems can enable all workers to benefit from the knowledge and strategies used by top-performing employees.

Collectly for Revenue Cycle Management

AI helps healthcare organizations improve cash flow due to accurate, timely, and comprehensible billing and patient billing support. AI can enhance staff capabilities with data analysis and pattern recognition, giving medical billers, coders, and finance and A/R teams more time for complex and strategic work.

Collectly is AI-powered RCM software that drives greater billing accuracy, higher revenue, and more predictable cash flow. Collectly offers:

• Patient-friendly billing with digital statements, integrated payments, and online self-service payment plans
• Automated billing workflows and reconciliation to fit your RCM process
• HIPAA-compliant patient messaging with unlimited text, email, and automated calls
• Easy payment options, like autopay, batch autocharge, and card on file
• Secure and compliant infrastructure, including HIPAA, SOC2, and HITRUST i1 Certification

Collectly works with any EHR or PM, making it easy to add powerful billing to any healthcare tech stack. More than 3,000 healthcare facilities use Collectly for revenue cycle management—with an average 75-300% increase in patient payments.

See how CleanSlate achieved a 650% ROI with Collectly and increased patient revenue by over 250% overall.

Revenue Cycle Management KPIs

Metrics used to measure the effectiveness of an organization’s revenue cycle management activities. Common revenue cycle KPIs include Days in Accounts Receivable (A/R), Net Collection Rate, Cost to Collect, and Cash Collection Percentage.