Further enhancements can be made by using robotic process automation (RPA) driven by artificial intelligence (AI), now often referred to as Intelligent Process Automation (IPA). Manual claims processing is a laborious, expensive process that requires the revenue cycle staff to review every denied claim and spend way too much time digging into each issue. It is also susceptible to human error.

Intelligent Process Automation that tightly integrates advanced analytics and RPA has the potential to automate significant portions of the claim resolution life cycle. As a result, providers are able to collect more of the reimbursement they’re due while reaping tremendous time and labor savings as well.


3. The maturing of big data and the data economy

You don’t have to be a master seer to predict that the big data in healthcare organizations will continue to grow. What many get wrong, however, is that it will continue to be managed in different parts of the organization rather than becoming totally or perfectly centralized.

This persistent decentralization is a result of the reality that great use of big data is about driving actions and improving outcomes rather than the technology itself. To produce meaningful results, users must understand the business problems and many complex use cases that lead to problems. This knowledge depends on the users having a deep understanding of how things really work within the organization.

Normally, this awareness is contained at the departmental or team level, which means individual departments must get good at big data and analytics if the organization is going to take advantage of them. They can achieve this expertise on their overtime or get there faster by working with a partner that already knows how to manipulate the components and take meaningful action based on what the big data and analytics show.

4. AI becomes more critical to productivity and revenue optimization

When working with big data, AI and ML offer many advantages — not the least of which is their ability to spot patterns in data humans might miss while achieving tremendous scale. Once AI and ML gain more “experience” with the data they can continue to improve their decision-making ability on their own rather than requiring human input. In fact, AI is able to learn and improve based on the outcomes from every closed account whether it was paid in full with no issues or there were discrepancies.

As pointed out previously, AI and ML can review a batch of claims and determine why they were denied (as well as whether the denial was appropriate) in seconds, once its knowledge base is constructed — rather than the hours or days it would take humans to work through the same batch.

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