Learn how AI Can Transform Prior Authorization Processes | Arkangel AI

Prior authorization (PA) poses significant challenges within the healthcare system. A survey conducted by the American Medical Association revealed that 30% of physicians have encountered serious adverse events due to PA requirements, with 94% experiencing delays in patient access to necessary care and perceiving negative impacts on patient outcomes. Physicians and their staff spend an average of two business days each week managing PA requests, highlighting the substantial time burden associated with the process (1). Conversely, according to America’s Health Insurance Plans, 98% of surveyed plans assert that PA improves care quality and supports evidence-based treatment, with 91% using it to enhance patient safety and 79% noting cost-saving benefits (2). However, administrative inefficiencies persist; only 12% of PA requests were fully electronic in 2018, while approximately half were processed manually via faxes and phone calls, contributing to an estimated $25 billion in administrative costs (3).

• 30% of physicians report serious adverse events due to prior authorization (PA), with 94% experiencing care delays and negative impacts on patient outcomes.
• Despite provider complaints, 98% of health insurance plans assert that PA improves care quality, supports evidence-based treatment, and enhances patient safety.
• Administrative inefficiencies persist, with only 12% of PA requests being fully electronic and substantial costs estimated at $25 billion annually attributed to PA processes.

To reduce the delay in approving patient treatment caused by the PA process, a predictive model called "FastTrack PA" has been designed to predict the outcomes of PA requests. By utilizing patient data and treatment details, FastTrack PA seeks to streamline the approval process, potentially reducing the administrative burden and associated costs.

The synthetic dataset for the model was constructed informed by industry research and articles, ensuring a realistic representation of conditions surrounding PA requests. The model leverages variables such as comorbidities, the severity of health conditions, and medication details, as analyzed in works by McKinsey & Company (1), Joseph from Forbes (2), and Psotka et al. from the Value in Healthcare Initiative's publication (3). These resources allow the model to simulate the intricacies of real-world PA processes.