Separating AI Hype from Clinical Reality AI Workshop | September 2026-Our Journey to Automate Prior Authorization

Pdf Summary

This case study describes a GI practice’s early effort to automate prior authorizations with AI, while emphasizing that the project is an implementation story—not proof of clinical benefit or product endorsement. The authors stress they are still in early adoption and do not yet have local outcome data on approval rates, denials, turnaround time, or ROI.<br /><br />The presentation explains why prior authorization was chosen: it creates heavy physician and staff burden, delays care, and contributes to denials and adverse events. In GI, the evidence shows substantial administrative strain, but key national metrics are still missing, so baseline measurement is essential before automation.<br /><br />The manual prior authorization workflow is broken into multiple handoffs: order placement, eligibility checks, evidence gathering, payer submission, response handling, and appeal/resolution. The automation target is not autonomous clinical decision-making, but repetitive administrative work such as rules matching, drafting, routing, tracking, and escalation.<br /><br />Their implementation strategy centers on “automate friction, not accountability.” AI may assist with pre-submission checks, case tracking, denial response drafting, and patient coordination, but humans retain control over clinical decisions and final submissions. Vendor selection is based on a workflow scorecard covering clinical fit, technical integration, governance, and evidence of value. A cross-functional team—GI, PA staff, IT, compliance, legal, and the vendor—was required before launch.<br /><br />The pilot was intentionally narrow: one service line, selected procedures, and relevant payers, with human review of all AI outputs. Success will be judged using access, efficiency, quality, safety, and financial metrics, especially whether the tool reduces net administrative burden and returns time to patient care.<br /><br />Key lessons include: measure baseline first, keep evidence traceable, design clear exception pathways, watch for failure modes like wrong payer rules and automation bias, and scale only after proving value. The closing message: if AI does not reduce burden, it is just another layer.

Asset Subtitle

Ekta Gupta, MD

Keywords

prior authorization

artificial intelligence

gastroenterology

workflow automation

administrative burden

payer rules

human oversight

implementation pilot

denial management

healthcare operations