For healthcare provider organizations exploring autonomous medical coding, knowing how to effectively assess the technology and evaluate potential vendors is essential.
This guide provides 10 fundamental questions to help healthcare providers better understand autonomous medical coding and select the right vendor for their coding automation needs.
Autonomous medical coding solutions leverage deep learning, natural language processing/rules-based techniques, or a hybrid approach (which combines deep learning with NLP and rules-based) to translate patient records into medical codes in seconds with zero human intervention.
A major step forward from human coders and computer-assisted coding (CAC), autonomous coding solutions improve coding efficiency, reduce operating costs, and accelerate revenue cycle management timelines while maintaining exceptional accuracy.
Meet Inova, the leading health system in Northern Virginia. Inova implemented Nym's autonomous medical coding engine for emergency department (ED) facility coding after struggling with medical coder staffing shortages and increasing emergency department patient volumes.
One year since deploying Nym's engine, Inova has completely transformed medical coding in its ED facilities.
Eliminated mandatory OT and limited PTO for medical coders
$500K+ reduction in annual ED medical coding costs
50% decrease in weekly revenue sitting in ED DNFB
10% increase in average charges per ED encounter