Cost-effectiveness of AI for pediatric diabetic eye exams from a health system perspective

Mahnoor Ahmed; Tinglong Dai; Roomasa Channa; Michael D Abramoff; Harold P Lehmann; Risa M Wolf

doi:10.1038/s41746-024-01382-4

Cost-effectiveness of AI for pediatric diabetic eye exams from a health system perspective

NPJ Digit Med. 2025 Jan 2;8(1):3. doi: 10.1038/s41746-024-01382-4.

Authors

Mahnoor Ahmed¹, Tinglong Dai^{2

3

4}, Roomasa Channa⁵, Michael D Abramoff^{6

7

8

9

10}, Harold P Lehmann¹, Risa M Wolf^{11

12}

Affiliations

¹ Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA.
² Carey Business School, Johns Hopkins University, Baltimore, MD, USA.
³ Hopkins Business of Health Initiative, Johns Hopkins University, Baltimore, MD, USA.
⁴ School of Nursing, Johns Hopkins University, Baltimore, MD, USA.
⁵ Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
⁶ Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA.
⁷ Digital Diagnostics Inc, Coralville, IA, USA.
⁸ Iowa City VA Medical Center, Iowa City, IA, USA.
⁹ Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA.
¹⁰ Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.
¹¹ Hopkins Business of Health Initiative, Johns Hopkins University, Baltimore, MD, USA. [email protected].
¹² Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA. [email protected].

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.

Abstract

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