Navigating the Complex Landscape of Prior Authorizations in Healthcare

By | September 28, 2023

Recently, a colleague shared an article with me, “The Two Words That Can Make Health Care a Nightmare,” by Chris Stranton, highlighting the challenges posed by prior authorizations (PAs) in healthcare. While I agree that PAs can be problematic, it’s crucial to delve deeper, and consider the broader context surrounding their use.

Prior authorizations have long been a contentious topic in the world of healthcare. There are instances where PAs serve a necessary purpose, controlling the utilization of tests, studies, and treatments that may be overused. Some physicians have earned a reputation for consistently making appropriate requests, while others may warrant closer scrutiny due to frequent denials. Balancing the debate over whether PAs should exist revolves around the question of how to implement them effectively. If we disregard the impact on insurance premiums, unchecked medical service ordering can become problematic and financially burdensome. Therefore, the key is to manage the complexity of the process and provide robust decision support upfront.

Prior Authorizations, as they stand today are somewhat of a blunt instrument. The automation of the PA process often relies on algorithms that may request information that needs to be corrected or completed. Take, for example, the realm of high-cost radiology, such as MRI and PET scans. Evidence suggests that there is a substantial overuse of these tests. Given their high volume and costs, exercising control over their utilization becomes essential to maintain affordability and quality. Furthermore, excessive testing raises concerns about patient safety and the overall quality of care.

An alternative approach to addressing the challenges posed by PAs would be to lower the unit costs of medical tests to a level where questioning their necessity becomes unnecessary. However, this approach only partially resolves the issue. It’s crucial to recognize that clinicians often operate with the belief that they must do what’s best for their patients, which sometimes leads to low-yield activities. This mindset clashes with insurance companies’ broader perspective, which aims to manage costs across a wide spectrum and provide affordable coverage. Additionally, more information only sometimes leads to changes in treatment plans. While additional details may impact prognosis, they may not significantly alter the treatment course. Balancing these considerations requires thoughtful conversations between clinicians, patients, and insurers.

As Artificial Intelligence (AI) continues to advance, it holds the potential to assist clinicians in making informed decisions about necessary studies and treatments. This advancement could lead to a sliding PA model based on a clinician’s history of approval rates. By decreasing the need for PAs and subsequently reducing their utilization, costs can be lowered without compromising outcomes—the ultimate goal of any healthcare system.

Lastly, contrary to popular belief, insurance companies do not necessarily favor PAs. They recognize that PAs can be problematic for their networks and members, often leading to dissatisfaction. In essence, if we aim to change behavior within the healthcare system, we must make the right actions easier to perform and the wrong ones more challenging. Effectively managing PAs and enhancing decision support tools would address both components of this equation, ultimately leading to better, more efficient healthcare delivery for all.