Understanding the Impact of AI on Revenue Cycle Management in Healthcare: Strategies for Achieving Financial Sustainability

Almost half of hospitals and health systems in the United States—about 46%, according to the AKASA/Healthcare Financial Management Association (HFMA) Pulse Survey—use AI in their revenue cycle management. Also, 74% of these places use some kind of automation, including robotic process automation (RPA) and AI tools. This shows that people realize manual and old methods are slow, repetitive, and often have human mistakes.

AI systems use technologies like natural language processing (NLP), machine learning, and robotic automation to help with important financial tasks such as coding accuracy, billing, claim management, and handling denied claims. For example, Auburn Community Hospital in New York saw a 50% drop in claims that were discharged but not billed yet. This happened because AI tools took care of many claim reviews, letting coders focus on more important work. Their coder productivity also went up by more than 40%, which means the staff worked better.

AI can quickly process lots of data so healthcare providers can find billing mistakes before claims are sent. Predictive analytics can guess which claims might be denied by insurance. Then, staff can act early to appeal or fix errors, which helps claims get approved more often. For example, Fresno’s community health network in California reported a 22% drop in denials needing prior authorization and an 18% drop in coverage denials after using AI for claim review.

These reductions help providers because money comes in faster and the staff have less work. When claims are denied or payments come late, it causes big delays in revenue. This affects budgets and makes it hard to use resources well.

AI Functions Enhancing Revenue Cycle Management

AI uses several key functions that change how work flows and help money matters:

• Automated Coding and Billing: AI uses NLP to read clinical documents and turn them into the right medical codes. This lowers coding errors that cause denied or late claims. Correct coding helps providers follow rules and get paid better.

• Claims Scrubbing: Before claims are sent, AI checks them for errors or missing details like prior authorizations. By automatically checking insurance coverage and rules, AI reduces claim denials. Banner Health, a big healthcare system, uses AI bots to find insurance info and manage communication with payers, making it easier to handle appeals.

• Denial Management and Predictive Analytics: AI spots patterns in denied claims and writes appeal letters based on insurance rules and past results. This cuts manual work and raises chances for payment. At Auburn Community Hospital, this AI help improved financial results and led to a higher case mix index.

• Revenue Forecasting and Payment Optimization: AI looks at past billing and payments to predict future money coming in. This helps managers plan budgets better. AI can also make custom patient payment plans and use chatbots to remind patients about bills, which helps collect payments more often.

• Data Security and Compliance Monitoring: AI helps keep data safe by checking that healthcare rules like HIPAA are followed. This lowers risks of data leaks and fines.

These AI features reduce the need for big claims processing teams and let staff do more important admin and clinical jobs. This helps healthcare groups run in a way that is financially stable.

AI and Workflow Automation: Enabling Better Revenue Cycle Management

AI also works well when combined with workflow automation. While AI makes smart decisions, automation makes sure tasks run smoothly from start to finish with little human help.

Robotic Process Automation (RPA) copies simple, rule-based jobs like checking eligibility, submitting claims, and registering patients. When AI is added, bots can handle more complex work by deciding about claim approvals or pointing out risky items for review by people.

For example, Auburn Community Hospital used RPA with machine learning and NLP to change its revenue cycle work. Automation cut “discharged-not-final-billed” cases by half. This means patient cases were quickly finished financially, which saved money that could have been lost. Also, coder productivity went up because automation took care of repetitive jobs like typing data and first claim checks.

Banner Health showed that AI bots can speed up insurance coverage checks. This kind of automation means manual tasks like insurance verification and getting prior authorization can be done faster and more accurately. Their automation includes models that predict if collections will work, so they decide which claims to chase or write off sooner.

Automated workflows with AI lower costs by cutting rework and using staff time better. This is very important in U.S. healthcare where worker shortages slow billing and cause more errors. Using AI in call centers for billing help and payment plans improved productivity by 15 to 30%, a 2023 McKinsey report says.

Real-World Results of AI Adoption in U.S. Healthcare Revenue Cycle

The money benefits seen in several U.S. hospitals and practices using AI show its potential to be used widely:

• Auburn Community Hospital improved coder efficiency, case mix index, and reduced backlogs by using AI and workflow automation in RCM.

• Banner Health made insurance processes easier with AI bots for coverage checks and automatic appeal letters. Their models for writing off bad debts helped make better financial decisions with the same staff.

• Fresno Community Health Network cut denials related to prior authorizations and saved time equal to 30-35 staff hours every week by using AI claim reviews.

These examples show AI helps not just by cutting admin work, but also by improving key financial numbers needed for long-term success.

Challenges and Considerations for Medical Practices and Health Systems

AI has many benefits but needs careful planning to use well. Risks include bias in AI programs, which could unfairly affect some patient groups if left unchecked. Automation systems without human checks can make mistakes that cause claim denials or break rules.

Healthcare groups should put human experts in place to check AI results before final decisions or submissions. This makes sure results are right while still saving time with automation.

Also, investments in AI need to match what the organization needs and can handle. Success depends on training staff, fitting AI with existing electronic health records (EHRs), and clearly explaining AI’s role in daily work.

Strategies for Leveraging AI to Achieve Financial Sustainability

For managers, owners, and IT leaders, using AI for revenue cycle management includes several steps:

• Assess and Prioritize Key Revenue Cycle Activities for Automation: Find tasks like eligibility checks, claim scrubbing, and denial management that take most time and resources. Start automating these first.

• Partner with Experienced AI and Workflow Automation Vendors: Pick solutions that work well in healthcare. Providers like eClinicalWorks offer AI-powered electronic health records and contact centers that help patient engagement and support RCM.

• Invest in Staff Training and Support: Make sure teams know how AI tools change work and how to read data results. Human review stays important to avoid errors and follow rules.

• Establish Clear Validation and Audit Processes: Regularly watch AI decisions to find bias or mistakes. Create ways to keep improving AI programs.

• Leverage AI for Patient Engagement: Use self-scheduling, telehealth, and payment chatbots to make patient interactions easier, lessen phone calls, and speed up payments.

• Integrate AI with Existing Technologies: Good RCM needs AI tools, EHRs, billing software, and payer portals to work together smoothly.

• Continuous Evaluation of Financial Impact: Track important numbers like denial rates, billing cycle time, and how much money is collected to see how AI helps and support future spending.

In Summary

Artificial intelligence is changing revenue cycle management in U.S. healthcare by cutting admin work, increasing accuracy, and helping staff work better. Hospitals and medical practices that use AI along with workflow automation can manage money better, reduce claim denials, and handle insurance payments more easily. Healthcare leaders should plan AI use carefully and keep human oversight to get the most benefit and keep costs down in revenue cycle work.