What If Your Prior Authorization System Is Stuck in 1999?

Maybe we shouldn’t party like it’s 1999 (all due respect to Prince).  Here’s a hot take: that “automated” prior authorization system your organization spent six figures on might just be a very expensive flowchart. Don’t take it the wrong way, rules engines have their place, and I’ve built plenty of them in my time managing clinical teams. But when your pharmacists are still drowning in faxed PDFs and scanned clinical notes, it’s worth asking whether your technology is actually solving the right problem.

Let me break down the difference. A rules engine operates on explicit if-then logic coded by humans: if the patient has tried Drug A for 30 days and it failed, then approve Drug B. It’s predictable, transparent, and works beautifully when your data arrives in neat, structured boxes. The catch? Healthcare data rarely shows up that way. According to International Data Corporation (IDC), roughly 90% of healthcare data is unstructured!  Think about physician notes, discharge summaries, and those infamous faxed documents that somehow still dominate our industry [1]. Rules engines are deterministic. They can only perform the functions they’ve been explicitly programmed for, which means they hit a wall when clinical context gets messy or ambiguous [2]. When a pharmacist has to manually dig through fifty pages of clinical documentation to find evidence of prior therapy failure, your rules engine isn’t really automating, it’s just waiting.

This is where artificial intelligence earns its keep. Unlike rules engines, AI systems don’t require humans to hand-code every decision pathway. Instead, machine learning algorithms identify patterns and relationships within data, enabling them to process unstructured information and adapt to new scenarios [3]. In practical terms, this means AI can read those scanned clinical notes, extract the relevant criteria, and surface the information a pharmacist actually needs to make a decision. At Epidaurus Health, our Asklepios AI processes prior authorization cases in an average of 60 seconds.  This includes synthesizing a patient summary and surfacing clinically relevant information across 354 unique medications and 16 therapeutic classes. The result? Organizations in our trials saw 30–35% time savings per case and capacity increases of 46–54%, all while maintaining 87% accuracy rates with human reviewers [4]. That’s not replacing clinical judgment; it’s removing the tedious data extraction that burns out even your best pharmacists.

The bottom line is this: rules engines excel when the logic is straightforward and the data is clean, but healthcare prior authorization involves neither of those things. AI doesn’t replace the need for clinical expertise.  It structures the chaos so your experts can focus on what they’re actually trained to do. If your team is spending more time hunting for documentation than evaluating clinical appropriateness, it might be time for a conversation. Visit us at https://epidaurus.health to learn how Epidaurus can help transform your prior authorization workflow.

References:

[1] IDC. “90% of enterprise data is unstructured.” Cited in Presidio, “Do More with Your Unstructured Healthcare Data,” September 2025. https://www.presidio.com/blogs/do-more-with-your-unstructured-healthcare-data-unlock-insights-reduce-burnout/

[2] WeAreBrain. “Rule-based AI vs machine learning: Key differences,” November 2025. https://wearebrain.com/blog/rule-based-ai-vs-machine-learning-whats-the-difference/

[3] Capital One. “A Modern Dilemma: When to Use Rules vs. Machine Learning.” https://www.capitalone.com/tech/machine-learning/rules-vs-machine-learning/

[4] Epidaurus Health. “From Backlogs to Real-Time Decisions: Asklepios AI Provides Scalable Clinical Support for Prior Authorization.” Case Study, 2025.