3 Barriers Stalling AI Adoption in Revenue Cycle Management

3 Barriers Stalling AI Adoption in Revenue Cycle Management

AI technology is quickly evolving and already surpassing human decision-making in certain instances; sometimes, in ways, we can’t explain. While many are alarmed by this, AI is producing some of the most effective and dramatic results in business today. 

Adoption of AI in healthcare is growing, but there are still barriers to overcome

AI can be defined in many ways but, broadly, it is the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and applying creativity. This includes subsets of AI such as machine learning (ML), predictive analytics, robotic process automation (RPA), natural language processing (NLP), and optical character recognition (OCR).

AI is not new to healthcare, but the market has traditionally fallen behind compared to other industries when it comes to adopting and using artificial intelligence (AI). Now, more and more healthcare leaders are turning to AI. In fact, a recent survey reports that nearly all hospitals expect to be using AI within three years for revenue cycle management (RCM). However, many have yet to adopt the technology due to how they perceive barriers related to it, and for those already using AI in RCM, its application is limited and doesn’t usually span the revenue cycle from end to end.  


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