Is CDI about to embark on a long journey to reinvent Itself? There is no arguing that artificial intelligence (AI) and natural language processing (NLP) are making inroads in the healthcare revenue cycle, creating better efficiencies with the automation of a multitude of historically manually performed tasks, thereby reducing positions that were once performed by staff. AI is clearly beginning to take hold and make significant inroads in the clinical documentation integrity (CDI) space. I have noticed serval posts on LinkedIn, as well as in Becker's Healthcare e-newsletters, discussing the role of AI in the revenue cycle. Just recently, there was a blog post published in KevinMD titled "How an AI bot transformed my EHR experience (KevinMD blog)" centering on how AI streamlined the provider's documentation and charting in the electronic health record (EHR) by scanning through the documentation as the note is being completed, providing suggested diagnoses with associated ICD-10 codes.

When rolling out new IT it's important to explain the reasoning behind a new implementation and ask for clinician input. When physicians feel like they are a cog in the wheel of implementation, they will resist.

We recently asked Alexa if she could code a few medical charts for us. "Sorry I don't know that." After all, the U.S. healthcare industry spends billions of dollars on 250,000 medical coders every year to do the job. This way of doing business might be error-prone, inefficient, and bound by constantly changing regulations, but hey, IT IS a solution. But you know what else is a solution?

The "old playbook" becomes increasingly obsolete in the revenue cycle. Historically revenue cycle leaders and CFOs could solve problems like prior authorization by adding FTEs, outsourcing to third parties, or retooling workflows. Those legacy levers are losing their efficacy, and as we see more CFOs capping internal headcounts and trimming operating budgets--while simultaneously increasing clinical volumes (perhaps via acquisition)--revenue cycle leaders will need alternative means of execution (i.e.

When it comes to artificial Intelligence and machine learning, most often we hear them discussed in a clinical context. But that's not the only realm where AI and ML could make an impact. In fact, revenue cycle is well-suited to AI and MI. According to Nick Giannasi, chief AI officer for Change Healthcare, it's almost the perfect problem for AI and ML to solve. "You basically have a lot of historical transactions, basically claims, and then information that comes back: what was paid, what wasn't paid, what was the reason. When you have that in large volumes, it's really hard to represent that with human knowledge or rules because there are lots of combinations," Giannasi said.

That's something all healthcare organizations must do, and do well, especially during a period of changing financial models and expectations. Revenue cycle management systems are bread and butter when it comes to what healthcare organizations need in health IT. But the next generation features and functions of revenue cycle management systems may be anything but ordinary, according to experts. For one thing, expect next-generation revenue cycle management systems to boast quite advanced analytics, said Kellye Sherbet, president of RCM services at Aprima Medical Software, which markets EHR, practice management and revenue cycle management systems for medical group practices. "Healthcare organizations require sophisticated analytics to perform a deep dive into their information and look at the margins for ancillary services provided," Sherbet said.

Artificial intelligence (AI) propelled by increasing availability of data and analytics is creating a revolution in the way technology works in solving complex problems. The fact that it utilizes, both structured and unstructured data to deliver powerful, conclusive result makes it highly sought after in areas of healthcare, entertainment, finance, transportation and more. Thanks to AI, the voluminous data which was previously untapped has now been unplugged. Coupled with predictive analysis, through AI massive amounts of data have been scrubbed to produce results that have made a paradigm shift in the way healthcare operates for all – providers, patients and professionals. What is AI actually and how does it work in healthcare?

Artificial intelligence: Savvy hospitals are deploying AI and its technological brethren cognitive computing and machine learning in specific use cases at this point – while industry luminaries are predicting that their advancement will soon start happening more quickly than previously anticipated. "I've never in my career seen the acceleration of technology as fast as what we've witnessed in machine learning during the last two years," said Dale Sanders, executive vice president at Health Catalyst. Sanders, it's worth noting, has a U.S. Air Force background working on stacked neural networks and fuzzy logic, which used to be called deep learning, as well as serving as the CIO of both Northwestern University and national health system of the Cayman Islands. "The rate of improvement happening in machine learning," Sanders added, "is way beyond what Moore's Law is to chips." Hospitals already deploying AI As the next generation of both patients and caregivers – including clinicians, doctors, nurses, specialists, even executives and administrators – starts taking a foothold in the healthcare workforce, hospitals looking for a first-mover advantage already know that AI is on the verge of becoming a critical component across the entire organization, and not just IT. "AI and machine learning are exciting opportunities for us to accelerate," Carolinas HealthCare Chief Information and Analytics Officer Craig Richardville said.

Artificial intelligence: Savvy hospitals are deploying AI and its technological brethren cognitive computing and machine learning in specific use cases at this point – while industry luminaries are predicting that their advancement will soon start happening more quickly than previously anticipated. "I've never in my career seen the acceleration of technology as fast as what we've witnessed in machine learning during the last two years," said Dale Sanders, executive vice president at Health Catalyst. Sanders, it's worth noting, has a U.S. Air Force background working on stacked neural networks and fuzzy logic, which used to be called deep learning, as well as serving as the CIO of both Northwestern University and national health system of the Cayman Islands. "The rate of improvement happening in machine learning," Sanders added, "is way beyond what Moore's Law is to chips." Hospitals already deploying AI As the next generation of both patients and caregivers – including clinicians, doctors, nurses, specialists, even executives and administrators – starts taking a foothold in the healthcare workforce, hospitals looking for a first-mover advantage already know that AI is on the verge of becoming a critical component across the entire organization, and not just IT. "AI and machine learning are exciting opportunities for us to accelerate," Carolinas HealthCare Chief Information and Analytics Officer Craig Richardville said.