The health care revenue cycle is transaction oriented. Patients build transaction history from scheduling to discharge, and the process continues through final payment and claim reimbursement. It is a complex system that demands an immense investment of time and resources. To function properly, it must be optimized at every level, and like many other industries, health care has settled on artificial intelligence (AI) to alleviate its revenue cycle management (RCM) burden.
AI and RCM pain points
If you’re considering applying an AI solution to your practice’s RCM issues, begin by evaluating when and where automation will serve you — and your patients — best. Health care RCM is complex and labor-intensive. Processing prior authorizations and health insurance claim denials are particularly time consuming. An AI solution can prevent and/or address:
- Prior authorization burden. AI can automate much of the process.
- Claim denials. AI can reduce errors that lead to denials and costs associated with refiling.
- Coding and billing. Automation reduces human error and increases efficiency.
- Scalability. Automation allows for expansion with fewer staffing additions.
- Interoperability. Effective automated RCM systems integrate seamlessly with existing technology.
AI’s potential for RCM optimization
AI operates with algorithms designed to find patterns in data and use them to plan for future positive outcomes. AI can leverage existing RCM data to address common issues — including processing prior authorizations, monitoring billing and claim status, and estimating out-of-pocket expenses. AI can automate RCM’s most complicated processes, pinpoint when and where human intervention is required, and assign necessary tasks to the most qualified personnel.
According to data from Accenture Analytics, AI solutions can save clinics $7 billion each year by automating a whole spectrum of RCM tasks — from the complex to the mundane.
Best practices for using AI for RCM
AI automates routine tasks and reduces the risk of human error, but it adds value in other ways as well. As RCM consultant Peter Joseph wrote in a 2020 article for Medical Economics, “One underappreciated element of successful AI tool implementation is the value of deep-dive account-level investigation. A big-picture data review can help point to large issues, but it isn’t until you look in-depth at individual examples and understand why denials happened that you can start to create data and turn anecdotal observations into a meaningful data set.” To take full advantage of an AI-enabled RCM system, consider these best practices:
- Define your success metrics. What does successful implementation of an AI look like for your practice?
- Use the data. Data analysis can show you which processes work efficiently — and which don’t.
- Identify high-touch processes. AI is most effective with processes that require frequent, but not necessarily manual, interaction.
- Reevaluate often. RCM circumstances and regulations are subject to frequent change. Be sure your AI system meets your RCM success metrics and processes data in accordance with current local, state, and federal regulations.
AI is an effective tool for fixing common RCM problems, but it’s easy to get carried away when considering its potential. Providers should start slow, automating specific accounting tasks and workflows. Gradual implementation allows you to track effective changes and adjust those that are less effective. And limited trials with high success rates demonstrate added value make the case for further investment in AI and other digital, technology-based RCM solutions.
Contact TruBridge to learn more about AI-enabled RCM solutions.
Written by Alexis Roberson
TruBridge Sr. Director, Revenue Cycle Solutions