Current Trends and Statistics on AI and RPA Usage in Healthcare: Insights from Leading Health Systems

Revenue cycle management (RCM) includes many tasks like patient registration, coding, billing, claim submission, payment collection, and handling denials. These tasks are hard and take a lot of resources. Artificial intelligence (AI) and robotic process automation (RPA) have become helpful tools to make these tasks easier.

A 2023 survey by Waystar and The Health Management Academy found that only 6% to 28% of large health systems use AI and RPA tools at different stages of their revenue cycle management. But this number is expected to grow quickly. About half of the top health systems said they plan to invest in AI and RPA tools for revenue cycle management in the next three years. Most, about 82%, say their main goal is to improve financial performance.

Another survey by the Healthcare Financial Management Association (HFMA) and AKASA showed that around 46% of hospitals and health systems already use AI in their revenue cycle work. About 74% have some kind of automation, including AI or RPA. This shows a clear move toward using digital tools to make revenue cycle tasks faster, more accurate, and more productive.

Since labor costs are rising across the country and healthcare profits are shrinking, administration teams must work more efficiently. AI and RPA help by increasing productivity, lowering mistakes, and cutting operational costs.

How AI and RPA Improve Financial Performance in Healthcare

The main reason health systems use AI and RPA is to improve their finances. Hospitals and medical groups that use these tools see many benefits:

• Reduction of Claim Denials: AI checks claims before they are sent out. It can find missing authorizations or incomplete documents. For example, Fresno’s Community Health Care Network cut its prior-authorization denials by 22% and denials for services not covered by 18% after they started using AI. This means fewer claims get rejected and payments come faster.
• Increased Coding Productivity: Auburn Community Hospital in New York saw a 40% increase in how much work its coders did after using AI-assisted coding tools. Automation helps coders by checking clinical notes, lowering human error, and making billing more accurate. This helps the hospital get more money.
• Faster Case Finalization: Auburn Community Hospital also cut discharged-not-final-billed cases by 50%. This means patient accounts get closed faster, which speeds up revenue and lowers collection delays.
• Automation of Appeals and Denial Management: Banner Health uses AI bots that check patient coverage details and automatically create appeal letters when claims are denied. This saves staff time and makes sure denials are handled quickly and well.
• Time Savings for Staff: Fresno’s healthcare network saves 30 to 35 hours a week on appeals work. This lets staff focus on harder or more important tasks. It also helps with staff shortages that many hospitals face.
• Predictive Analytics for Financial Decisions: Banner Health uses AI models that predict if certain write-offs based on denial codes and payment chances are correct. This helps with better financial planning and managing risks.

Health systems that use AI and RPA say they are more satisfied with how they handle revenue cycle management compared to those not using these tools or just thinking about it.

Statistics Reflecting Current Healthcare AI and RPA Use

Here are some key numbers showing how AI and RPA are used in healthcare across the United States:

• 46% of hospitals and health systems use AI for revenue cycle management operations now.
• 74% of hospitals have some kind of automation in their revenue cycle, including AI or RPA.
• Health systems using AI and RPA report 15% to 30% more productivity in call center work. Front-office teams handle many patient calls about scheduling, billing, and insurance.
• One hospital reported 50% fewer discharged-not-final-billed cases and over 40% more coding productivity after using AI.
• AI screening helped reduce prior-authorization denials by 22% and denials for non-covered services by 18%.
• Automation of appeals and denials saved about 30 to 35 staff hours each week.

These numbers show how AI and automation help make operations better in both clinical and administrative areas.

AI and Workflow Automation: Optimizing Front-Office Phone Operations and More

One growing use of AI in healthcare is automating front-office tasks that need lots of staff time and repeat work. Simbo AI is a company that works on AI-driven phone automation and answering services for healthcare providers.

AI phone automation can handle patient calls about making appointments, reminders, basic billing questions, and insurance checks. These calls usually take a lot of staffing and can cause long wait times, which frustrate patients and staff. AI uses natural language processing (NLP) and machine learning to understand what patients ask, give quick answers, and send harder questions to human staff when needed.

Health administrators and IT managers can expect benefits from AI phone systems, like:

• Less Call Volume for Live Staff: Routine questions get handled automatically, freeing staff for harder patient issues and other important tasks.
• More Patient Satisfaction: Callers get answers faster and at more times, helping keep patients and reduce missed appointments.
• Lower Costs: Having fewer front-office workers lowers expenses for staffing, training, and replacing employees.
• 24/7 Service: AI answering can work outside office hours, letting patients get info or set visits even when offices are closed.
• Better Accuracy in Data: AI reduces mistakes during calls by logging correct info into health records or practice systems.

Besides phone work, AI workflow automation helps with scheduling, prior authorizations, insurance checks, and patient billing. These tools cut down on manual data entry, reduce mistakes, and speed up revenue processes. This leads to better finances and smoother operations.

Challenges and Considerations for AI Adoption in Healthcare

Even with its benefits, the use of AI and RPA in healthcare is not without problems. Studies show a gap between how useful these tools are thought to be and how much they are actually used. This slows down investments and full use.

Some challenges for healthcare groups include:

• Hard to Set Up: Linking new AI systems with current electronic health records (EHR) and billing software can be tough.
• Change Management: Staff may resist new ways of working and worry about their jobs, slowing down acceptance.
• Data Privacy and Security: AI tools must follow rules like HIPAA to protect patient information.
• Bias and Accuracy Risks: AI can give biased results if it is trained on incomplete or not-mixed data. Humans must check the results carefully.
• High Costs: Buying AI and RPA tools costs a lot, especially for smaller or rural healthcare providers.

To handle these problems, health system leaders should carefully pick vendors, plan training, and keep checking AI tools to make sure they work as expected.

The Future of AI and RPA in US Healthcare Systems

Reports from groups like McKinsey & Company and the American Hospital Association say AI use in healthcare revenue management will keep growing. Generative AI will likely do simpler tasks at first, like handling prior authorizations and writing appeal letters. In about five years, it may help more with decisions and working with patients.

Health systems that invest now in scalable AI and RPA can keep up with rules, payer complexity, and patients wanting easier services. Using AI phone automation and revenue cycle tools will stay important for improving how things work and financial results.

For medical practice admins, owners, and IT managers, knowing current trends and making smart choices about AI and RPA can help with daily operations and stronger finances.

Simbo AI’s focus on front-office phone automation suits these changes well. It helps healthcare providers in the United States cut administrative work while improving patient communication and experience.

By using AI not only in revenue tasks but also in front-office patient contact, medical practices can work toward a system that balances technology, saving money, and good service.