Use Cases for applying Artificial Intelligence in the Revenue Cycle
The adaptation of artificial intelligence in healthcare is ubiquitous, ranging from disease diagnosis, primary care, electronic health records, and telemedicine. But the usage of AI in RCM can be a gamechanger and help healthcare entities improve patient experience, improve collections, improve compliance, and reduce the workload of its RCM team members.
A recent survey of healthcare leaders indicates:
As many as 75% of healthcare leaders are actively implementing or planning to execute an AI strategy.
While 43% say, their first focus area will be automating business processes, such as revenue cycle management functions, to reduce costs.
Healthcare business processes are often complicated on account of the different rules governing charges and reimbursements. And this is a perfect scenario for the application of artificial intelligence. Guided by historical actions, AI can mimic most of the human actions in manual transaction processing environments. AI can increase revenue and reduce denials by mimicking the patterns of success it observes in the past data.
What is Artificial Intelligence?
Artificial Intelligence is the process of programming machines, i.e., computer or robotic devices, to perform activities that usually require human intelligence and judgment.
Applications of AI in Revenue Cycle Management
The Revenue Cycle is a highly transactional, rules-based environment – a patient interacts with the healthcare entity at multiple points from scheduling to treatment. The healthcare entity creates and submits claims and eventually gets paid. The possibility of converting all processes in the revenue cycle chain to a systemized set of action items that computers can mimic makes it an ideal candidate for the application of Artificial Intelligence. AI can leverage such tagged data to understand, learn, and perform the process, whether matching a patient with the right provider, estimating out-of-pocket costs, or coding the claim.
AI leverages a long-list of variables such as reimbursement history for similar claims and applying the right medical codes associated with them. Fortunately, AI is good at evaluating those variables and evolving the success rate of getting to the right outcome.
