It’s not surprising we tend to emphasize the clinical gains that AI promises moving forward, but AI technologies can do more for healthcare than improve an array of diagnoses and, ideally, better health outcomes.
As Matthew Hawkins CEO of Waystar, a healthcare revenue management firm, recently pointed out in a commentary at Forbes, the reality is that healthcare is a multi-faced industry, with “immense and complex” financial and administrative divisions, and those sectors are tapping AI and machine learning (ML) to revolutionize a range of processes that “directly affect provider efficiency and productivity.”
For example, Hawkins observes that “(p)roviders are always striving to keep denials and rejections to a minimum while processing them at a speed that keeps the practice running.” With AI and ML, healthcare organizations “can predict denials with a high degree of accuracy and precision. Building that intelligence into billing office workflows before claims are submitted helps organizations significantly reduce denials and appeals — and the time and cost associated with them.”
Further down the line, AI is also being used to make claim settlement more efficient. Says Hawkins, “AI can tell you how long a payer takes to settle a claim. It also can show when a claim has taken an unusually long time to get paid.”
For those concerned that AI, with its greater speed and efficiency, may be threatening human jobs, Hawkins says it’s more “about AI finding the point in the process where human intervention is needed,” then about AI replacing jobs altogether.
Similarly with critical data entry tasks, Hawkins points out that data entry typos and mis-entries are common, potentially dangerous and increasingly expensive in healthcare. The good news, he says, is that “it is in these routine parts of healthcare that AI/ML can make a huge difference. AI tools can detect where data entry mistakes may happen and work to either correct them or alert humans. Machines also have a perfect memory — meaning as records are moved or copied, AI tools can constantly be checking them for consistency and accuracy.”
Not surprisingly, the healthcare sector is still some distance away from making optimal use of AI on the financial and administrative sides.
As Hawkins sees the matter, “(d)ecision-makers may not know what tools exist, so it is up to forward-thinkers in each organization to stay informed and bring new technologies to the table for discussion.”
Moreover, even if organizations are aware of emerging technologies, Hawkins points to an array misconceptions. For example, some may “worry that AI will replace human intelligence, wresting control and judgment calls from human teams. The truth is AI augments human ability, handling monotonous and time-consuming tasks with less error. This frees up human resources for more elevated work, such as complex cases that require human discernment or conscience and overarching strategy.”
The bottom line, he says, is that “AI and ML are already making a big difference in healthcare, bringing order, predictability and efficiency to a revenue system that has historically been difficult to wrangle. These gains, enabled by AI and ML, will continue to be drivers for hospital revenue and a more transparent, improved patient financial experience.”