AI in patient billing: what you need to know

One out of every five medical group leaders say they will outsource and/or automate elements of their revenue cycle in 2024. Areas ripe for innovation? Self-pay collections, patient communications, medical coding, claim submission, and more.

This shouldn’t necessarily come as a surprise. Rising healthcare costs—coupled with ongoing administrative staff shortages—have prompted medical practices to explore artificial intelligence (AI)-driven revenue cycle management (RCM) solutions to promote operational resiliency. By ‘AI’ solutions, we mean technology that introduces an element of human intelligence (e.g., reasoning, learning, problem-solving, and decision making). This could include predictive analytics, machine learning, natural language processing, generative AI, or some combination of all four.

In addition to increasing operational efficiencies, AI-driven solutions help contain costs, enhance employee satisfaction, and improve the patient experience (and patient retention). AI technology also promotes revenue integrity and growth through fewer denials and a smoother cashflow. 

Best practices strategies for using AI in patient billing

As you continue to form an AI strategy in your medical practice, it’s helpful to keep the following five best practices in mind:

1. Choose an AI solution that addresses your medical practice’s specific patient billing pain points. For example, if your medical practice struggles with claim denials—especially denials from payers that use automated decision-making systems—you may want to consider an AI solution with predictive analytics to anticipate what claims payers may deny (and why) so you can fix errors proactively. In addition, you may want to consider a generative AI solution to create original, fact-based appeal letters to health insurers.
   
   On the other hand, if your medical practice struggles with patient collections, you may want to consider an AI solution with machine learning to tailor and personalize patient communications based on previous actions, preferences, and other factors. The goal? To foster greater engagement.
   
   If your patient experience surveys continually point to a problem with long wait times on the phone, this could be a cue to leverage an AI solution with natural language processing and interactive voice response (IVR) or live chat smart responses. Doing this can be especially helpful in the beginning of the year when annual healthcare deductibles reset, and patients tend to have a lot of questions.
   
   With IVR, patients and others interact with a computer-generated voice using keypad inputs or voice commands to accomplish important tasks in a fraction of the time. Interestingly, in January 2023, nearly four in 10 medical group leaders said they planned to optimize or implement major changes to phone systems and/or contact centers in the wake of spiking patient volumes.
   
   Not sure of your patient billing pain points? Look at internal data (e.g., revenue cycle management reports, practice management reports, and patient surveys), industry trends, and benchmarks. 
   
   
2. Calculate return on investment (ROI). How much time do staff spend on specific revenue cycle tasks? What do those staff earn per hour? If AI technology can absorb some or all that work, how much time (and money) would your medical practice save?
   
   
3. Ask for a demo. Ensure the vendor can mirror your current or proposed workflow and that the AI technology will fit into your existing revenue cycle technology stack. Has the AI solution been trained on similar processes and data? The demo is an opportune time for you to ask questions and ensure the solution will do exactly what you need it to do. Also be sure to ask for references. 
   
   
4. Ensure human oversight. AI technology is most successful when revenue cycle staff are tasked with verifying the output and continually providing a feedback loop for improvement. AI solutions should complement workload and workflow. 
   
   
5. Bill with empathy when using AI. There must be a balance between technology and human interaction. For example, while AI solutions can increase patient engagement through personalized billing statements and communications, there will always be a need for knowledgeable, empathic revenue cycle staff who can answer questions. With that said, AI may necessitate the need for staff education and upskilling. How ready is your revenue cycle workforce to perform the work of the future alongside technology? Where are the skill gaps, and what can your medical practice do to close them? 

Looking ahead

As you continue to explore AI technology in patient billing, it’s important to identify pain points, vent vendors fully, calculate an ROI, and train your RCM teams properly. AI in patient billing is no longer optional. It’s necessary to overcome operational challenges and promote financial sustainability. Learn how Inbox Health can help.

Lisa A. Eramo, MA is a freelance healthcare writer who specializes in healthcare reimbursement, health information management, value-based care, and patient engagement. She contributes bylined articles to various healthcare trade publications and also assists clients with healthcare content marketing. You can reach her at lisa@lisaeramo.com or by visiting www.lisaeramo.com.