One of the hottest buzzwords you've probably heard tossed around healthcare today is Artificial Intelligence (AI). It was even one of the key topics discussed at HIMSS18 as well as being the subject of a recent conversation with FDA Commissioner Scott Gottlieb. AI has the potential to truly revolutionize an industry sorely in need of a technological kick. While there are many components to effectively implementing and maintaining AI, the possibilities seem endless once it becomes actively rooted into front and back-end healthcare processes. Perhaps the biggest potential impact that doesn't get as much hype and attention is AI's role in the healthcare revenue cycle.
All too often, medical billing errors cost healthcare systems thousands, if not millions, of dollars. This has a directly negative effect on the revenue cycle and can have long-lasting consequences should it go untreated. That's where AI can jump in. Not only can AI create a fluid stream of communication between payers and medical departments, but it can seal any potential leaks that might cause vital information to be lost during different phases of the cycle. It's not just practices and hospitals looking at AI though, as MACRA is also jumping in on the frenzy to help push quality improvements.
Quality improvement is an undervalued aspect of the revenue cycle that will only continue to evolve as MACRA continues to change it's quality measures. AI is the logical next step in taking these programs where they want to be. AI can help transform these quality measures into something that is applied naturally as opposed to something that feels forced. This can be done through automation of health systems that track physician productivity, efficiency, and patterns. The more insight a hospital or practice has into the business aspect of the physicians, the better.
Controlling the power of AI's ability to automate processes is crucial in maintaining the revenue cycle. After all, the more automated healthcare processes are, the more patient-oriented a physician can be. Take for example the ICD-10 coding. Without proper coding methods in place, physicians either miss out on revenue due to undercoding or come under audit for overcoding. AI offers a path to easier coding and classification of diagnoses, procedures, and symptoms with better speed and accuracy. Using natural language processing, AI can match any information from a consultation into a medical dictionary and pull the corresponding codes. While coding is done at the earlier stages of the revenue cycle, it has one of the bigger impacts on hospital or practice revenue earnings.
The healthcare industry is constantly evolving and, as a result, the revenue cycle must as well. With the number of patients increasing and the drive for better quality care, there has never been a more opportune time to improve both back and front-end processes through AI. AI can go a long way in helping to increase efficiency throughout the revenue cycle, making it a tool that healthcare needs to adopt industry-wide sooner than later.
