Why Prior Authorization Reform Will Fall Short Without Clinically Trained Agentic AI
Published:
January 20, 2026
Published in Healthcare IT Today by Gigi Yuen, PhD, Chief Data and AI Officer at Cohere Health
Overview:
In a new byline for Healthcare IT Today, Gigi Yuen, PhD, Cohere Health’s chief data and AI officer, explains why upcoming 2026 CMS requirements and AHIP-HHS commitments will drive a fundamental shift in how health plans approach prior authorization. While many organizations are turning to automation to meet new standards, Gigi explains that generic AI tools lack the clinical reasoning needed to deliver safe, consistent, and compliant decisions at scale.
True modernization requires clinically trained, agentic AI that can interpret medical documentation, apply evidence-based guidelines, and act with transparency. Without clinical intelligence at the core, automation risks accelerating the very problems plans are trying to solve: inconsistent decisions, provider abrasion, and care delays.
Key themes covered:
- Why superficial automation is not enough: Digitizing legacy workflows does not fix deeper issues like siloed data, inconsistent policy interpretation, and lack of clinical context.
- The role of clinically trained agentic AI: AI must be built with physician oversight, large-scale clinical evidence, and transparent decision pathways to ensure accuracy, safety, and trust.
- Embedding trust and safety into workflows: Clinician-informed design, traceable audit trails, calibrated risk tiers, and clear explainability are essential for responsible AI.
- Reducing burnout through intelligent automation: Clinical-grade AI can automate routine decisions, freeing clinicians to focus on complex cases and reducing administrative burden.
- Why reform requires measurable outcomes: CMS and AHIP commitments call for observable improvements in turnaround times, appropriateness, transparency, and member experience. Clinically trained AI helps plans achieve these goals.
- The opportunity ahead: With 2026 as a catalyst, health plans can use clinically trained AI to transform prior authorization from a bottleneck into a streamlined, patient-centered process.
Read the full article
Written by
Gigi
Yuen, PhD
Gigi Yuen serves as Cohere Health’s Chief Data & AI Officer, leading the data organization, which includes data management, data science, machine learning/AI, and business intelligence.
Gigi has more than 20 years of experience leading cross-functional teams in data solution innovation. Prior to joining Cohere Health, she established the first-ever AI and Analytics function in Availity. At IBM, Gigi was recognized as a Distinguished Engineer. She became one of the first data scientists to receive this designation that is reserved for only the top 0.2% of technical staff in the global firm. Additionally, she led R&D for Watson Health’s $100M+ analytic portfolio where she drove advancements in patient care, real-world evidence research, and actuarial modeling. Gigi is an author of more than 15 peer-reviewed publications and has nearly 20 patents granted or pending.
Gigi holds a bachelor’s, master’s, and Ph.D. in Engineering from Northwestern University.
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