5 Must-Have Features for AI Platforms in RCM

5 must-have features and functions that health systems should look for when considering AI-powered tech to maximize revenue cycle performance. Every healthcare institution must have a sound, consistent revenue cycle to succeed. Yet, managing the revenue cycle necessitates skilled coders, thorough documentation, and prompt denial resolution. Over 30% of U.S. healthcare expenses are attributable to administrative processes, so every aspect of a patient encounter from check-in to billing and claims can be improved for efficiency. Computer-assisted coding (CAC) has been a standard practise in healthcare organisations of all sizes during the past 20 years or so. The revenue cycle management (RCM) method incorporates other breakthroughs in automation and artificial intelligence (AI) thanks to the use of natural language processing (NLP) to evaluate medical papers and identify relevant terms and phrases. AI algorithms analyse patient data to reduce some of the uncertainty in coding. Revenue Cycle Management (RCM) is the process of managing the financial aspects of a patient's healthcare experience, from the initial appointment scheduling to the final payment collection. AI can improve RCM for hospitals in several ways:

• Claims Management: One of the most time-consuming aspects of RCM is claims management. AI can help automate the claims management process, including claims submission, claim adjudication, and claims denial management. This can help hospitals reduce claim processing times, decrease claim denials, and improve overall revenue cycle efficiency.
• Patient Eligibility Verification: AI can help hospitals automate the process of verifying patient eligibility and benefits before services are rendered. This can help reduce claim denials and ensure that patients are not left with unexpected medical bills.
• Price Transparency: AI can help hospitals improve price transparency by providing patients with cost estimates for healthcare services based on their insurance coverage and other factors. This can help patients make more informed decisions about their healthcare and reduce the risk of surprise medical bills.
• Predictive Analytics: AI can help hospitals use predictive analytics to identify potential revenue cycle issues before they occur. For example, AI can help hospitals identify patients who are at risk of non-payment or who may require financial assistance. This can help hospitals develop more effective collection strategies and reduce the risk of bad debt.
• Intelligent Billing: AI can help hospitals improve the accuracy and efficiency of their billing processes by automating billing workflows and identifying billing errors. This can help reduce billing errors and improve overall revenue cycle performance.

In summary, AI can help hospitals improve the efficiency and effectiveness of their revenue cycle management processes, resulting in improved financial performance and better patient experiences.