Cost-effectiveness of AI for Pediatric Diabetic Eye Exams from a Health System Perspective

Overview

Journal NPJ Digit Med

Specialty Medical Informatics

Date 2025 Jan 3

PMID 39747639

Authors

Mahnoor Ahmed

Tinglong Dai

Roomasa Channa

Michael D Abramoff

Harold P Lehmann

Risa M Wolf

Affiliations

Soon will be listed here.

Abstract

Autonomous artificial intelligence (AI) for pediatric diabetic retinal disease (DRD) screening has demonstrated safety, effectiveness, and the potential to enhance health equity and clinician productivity. We examined the cost-effectiveness of an autonomous AI strategy versus a traditional eye care provider (ECP) strategy during the initial year of implementation from a health system perspective. The incremental cost-effectiveness ratio (ICER) was the main outcome measure. Compared to the ECP strategy, the base-case analysis shows that the AI strategy results in an additional cost of $242 per patient screened to a cost saving of $140 per patient screened, depending on health system size and patient volume. Notably, the AI screening strategy breaks even and demonstrates cost savings when a pediatric endocrine site screens 241 or more patients annually. Autonomous AI-based screening consistently results in more patients screened with greater cost savings in most health system scenarios.

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