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AI-driven assistance is most effective when applied throughout high-touch processes, or those routes that are often problematic and need regular and persistent intervention.
FREMONT, CA: Artificial intelligence (AI) is a notion that has moved swiftly from the sphere of science fiction into real and practical function in several industries, including healthcare. One useful application for AI-powered tools is in revenue cycle management.
Intricate AI technologies can sort through massive amounts of information and parse distinctions in ways that can make jobs like claims and denials management more effective. Understanding how the tools work and best practices connected with their introduction and optimization must be a priority for healthcare professionals.
When it comes to employing AI to capitalize on workflow in revenue cycle management systems, one essential requirement is to understand the core challenges. Firms can use outcomes-based analytics that appends context to the information and determine what is working and not.
Payer denials of submitted claims, for instance, entail a rich set of data that can indicate a wide range of potential issues. The problem could be in the claims process, documentation, training, or execution.
If one does not understand the root of the problem, they will not be able to design an efficient solution or even determine if an AI tool can be a prolific remedy. AI-driven assistance is most effective when applied throughout high-touch processes, or those routes that are often problematic and need regular and persistent intervention.
Another paramount practice in applying AI solutions is to identify success, and the metrics used to measure the success—early in the process. This is to reduce the days for average claim submission lives in the system or decrease the number of denials.
Finally, once a solution is put in place, it is important to follow up as rules and conditions change rapidly, particularly in fast-evolving setting of revenue cycle management. Besides, what works today may not be effectual in the long term, and it is vital to maintain data and revenue cycle watchfulness going forward.