AUTHOR=Goldstein Juli , Weitzman Dena , Lemerond Meghan , Jones Andrew 

TITLE=Determinants for scalable adoption of autonomous AI in the detection of diabetic eye disease in diverse practice types: key best practices learned through collection of real-world data

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 5 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2023.1004130

DOI=10.3389/fdgth.2023.1004130

ISSN=2673-253X

ABSTRACT=Autonomous Artificial Intelligence (AI) has the potential to reduce disparities, improve quality of care, and reduce cost by improving access to specialty diagnoses at the point-of-care.  Diabetes and related complications represent a significant source of health disparities. Vision loss is a complication of diabetes, and there is extensive evidence supporting annual eye exams for prevention.  Prior to the use of autonomous AI, store-and-forward imaging approaches using remote reading centers (asynchronous telemedicine) attempted to increase diabetes related eye exams with limited success. 
In 2018, after rigorous clinical validation, the first fully autonomous AI system (IDx-DR, Digital Diagnostics, Coralville, Iowa, USA) received U.S. Food and Drug Administration (FDA) De Novo authorization.  The system diagnoses diabetic retinopathy (including diabetic macular edema) without specialist physician overread at the point-of-care.  In addition to regulatory clearance, reimbursement, coverage and quality measure updates, successful adoption requires local optimization of the clinical workflow.    
The goal of this review is to evaluate workflow determinants and identify common themes leading to successful adoption, measured as average number of exams per month using the autonomous AI system.  A cross-sectional, mixed-methods evaluation was conducted comparing implementations and interventional strategies across four different US health centers over a 12-month period. Healthcare centers were geographically dispersed across the Midwest, Southwest, Northeast, and West Coast.  
After one year, the aggregated number of autonomous AI diabetes-related exams per month increased from 89 after the first month of initial deployment to 174 across all sites and individual centers nearly or more than doubled monthly utilization after providing the interventions outlined below.  
We uncovered three primary determinants for sustainable adoption: (1) Executive and Clinical Champions; (2) Underlining Health Center Resources; and (3) Clinical workflows that contemplate patient identification (pre-visit), IDx-DR Exam Capture and Provider Consult (patient visit), and Timely Referral Triage (post-visit). 
In addition to regulatory clearance, reimbursement, coverage, and quality measure updates, our evaluation shows that addressing the core determinants for workflow optimization is an essential part of large-scale adoption of innovation.  These best practices may be generalizable to AI systems in front-line settings, thereby increasing patient access, improving quality of care, and addressing health disparities.