You might imagine that only persons involved in high-end technical jobs are dealing with Artificial intelligence (AI), but it's actually used widely in an array of industries. For example, in consumer cameras like the Sony A6600, AI aids in identifying specific faces and eyes in its follow-focus feature. AI plays a role in everything from image recognition to data analysis.
The revenue cycle management (RCM) field is feeling the benefits of AI already, using the tech to sift through huge amounts of data and identify even the most subtle of data-shifts and trends in ways that can reveal a whole new level of effectiveness -- and efficiency. Other benefits include:
- Significant automation of repeatable tasks (as learned in a previous post, can utilize RPA) - but when manual intervention such as data extraction from forms or EOBs is needed, leave it to AI to find the most impactful intervention point
- Active prioritization in denials workflow can optimize account-level payment and denial risk factors
- Workflow specialization can strategically assign accounts to staff with the most relevant, specialized and advanced skill-sets to maximize results
Medical Economics has posted an article that provides a look at ways RCM professionals can best leverage AI as a valuable tool. While AI tech is indeed amazing, it is important to deploy it wisely:
Payer denials of submitted claims, for example, involve a rich set of data that could indicate a wide range of potential problems. The issue could be in the claims process itself, the documentation, the training, or the execution. But if you don’t understand the root of the problem, you won’t be able to design an effective solution—or even determine if an AI tool could be a productive remedy. AI-driven support is likely to be most effective when implemented throughout high-touch processes, or those processes that are regularly problematic and require frequent and continued intervention.
Another best practice in applying AI solutions is to define success—and the metrics you will use to measure that success—early in the process. Perhaps it’s to reduce the number of days for the average claim submission lives in the system, or to reduce the number of denials. Define success, measure your baseline well, and be clear about your desired outcomes.
OrboGraph has, of course, embraced AI along with Machine Learning and Deep Learning, in both healthcare RCM and banking. AI coupled with self learning improves as you utilize the system, so early adopters are gaining distance from the pack every day. The full and true potential of AI is realized with the collaboration between revenue cycle experts and data experts. Check out this video detailing how AI is deployed in RCM payment automation.