how Foresight Automates rcm: What Actually Works

Every week, we hear the same question from telehealth providers: "Should we use rules-based automation or AI for our revenue cycle?"

It's the wrong question.

Here's why: A Parkinson's disease telehealth visit generates a predictable pattern. The provider is licensed in Texas. The patient is at home in Texas. The visit is synchronous via video. These facts don't require AI to verify—they're deterministic. If patient location = Texas AND provider license state = Texas AND visit type = video, then apply POS 10 (patient's home) + Modifier 95 (synchronous telemedicine). This rule will fire correctly 100% of the time.

But that same visit also has a 32-minute consultation note with unstructured clinical documentation. Extracting "G20.A1 - Parkinson's disease without dyskinesia" from narrative text like "Patient presents with pill-rolling tremor at rest, bradykinesia noted during finger tapping, cogwheel rigidity in upper extremities" absolutely requires AI. No rule can map that clinical description to the correct ICD-10 code with sub-classification.

The insight that drives modern RCM automation: These aren't competing approaches. They're complementary capabilities that solve fundamentally different problems.