Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes

Abstract

Importance: Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D) and type 2 diabetes (T2D), yet screening rates remain low. Point-of-care diabetic retinopathy screening using autonomous artificial intelligence (AI) has become available, providing immediate results in the clinic setting, but the cost-effectiveness of this strategy compared with standard examination is unknown.

Objective: To assess the cost-effectiveness of detecting and treating diabetic retinopathy and its sequelae among children with T1D and T2D using AI diabetic retinopathy screening vs standard screening by an eye care professional (ECP).

Design, setting, and participants: In this economic evaluation, parameter estimates were obtained from the literature from 1994 to 2019 and assessed from March 2019 to January 2020. Parameters included out-of-pocket cost for autonomous AI screening, ophthalmology visits, and treating diabetic retinopathy; probability of undergoing standard retinal examination; relative odds of undergoing screening; and sensitivity, specificity, and diagnosability of the ECP screening examination and autonomous AI screening.

Main outcomes and measures: Costs or savings to the patient based on mean patient payment for diabetic retinopathy screening examination and cost-effectiveness based on costs or savings associated with the number of true-positive results identified by diabetic retinopathy screening.

Results: In this study, the expected true-positive proportions for standard ophthalmologic screening by an ECP were 0.006 for T1D and 0.01 for T2D, and the expected true-positive proportions for autonomous AI were 0.03 for T1D and 0.04 for T2D. The base case scenario of 20% adherence estimated that use of autonomous AI would result in a higher mean patient payment ($8.52 for T1D and $10.85 for T2D) than conventional ECP screening ($7.91 for T1D and $8.20 for T2D). However, autonomous AI screening was the preferred strategy when at least 23% of patients adhered to diabetic retinopathy screening.

Conclusions and relevance: These results suggest that point-of-care diabetic retinopathy screening using autonomous AI systems is effective and cost saving for children with diabetes and their caregivers at recommended adherence rates.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Abramoff reported being the founder, executive chairman, director, and an investor in Digital Diagnostics (formerly IDx) and having a patent assigned to Digital Diagnostics and University of Iowa that is relevant to the content of this article. No other disclosures were reported.

Figures

Figure 1.. Schematic of Decision Tree

Patients enter the model with the following options: autonomous artificial intelligence (AI) or eye care professional (ECP) screening. In the latter, they undergo or do not undergo an examination. The screening result may be positive or negative, with concomitant outcomes. If they do not undergo an examination, they either have or do not have diabetic retinopathy. If the AI screen is not diagnosable or the result is positive, patients are referred to an ECP; otherwise, outcomes are as indicated. The payoffs in this figure focus on true-positive results and out-of-pocket costs for the examination and, in the case of a positive ECP examination result, downstream treatment of diabetic retinopathy (1 visit).

Figure 2.. One-way Sensitivity Analysis for Probability of Eye Care Professional (ECP) Screening

AI indicates artificial intelligence; ICER, incremental cost-effectiveness ratio.