AI RESEARCH
Anterior's Approach to Fairness Evaluation of Automated Prior Authorization System
arXiv CS.AI
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ArXi:2603.14631v1 Announce Type: cross Increasing staffing constraints and turnaround-time pressures in Prior authorization (PA) have led to increasing automation of decision systems to PA review. Evaluating fairness in such systems poses unique challenges because legitimate clinical guidelines and medical necessity criteria often differ across graphic groups, making parity in approval rates an inappropriate fairness metric. We propose a fairness evaluation framework for prior authorization models based on model error rates rather than approval outcomes.