[Skip to Content]
[Skip to Content Landing]
Views 577
Citations 0
Original Investigation
September 3, 2020

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

Author Affiliations
  • 1Department of Pediatrics, Division of Pediatric Endocrinology, Johns Hopkins School of Medicine, Baltimore, Maryland
  • 2Department of Ophthalmology, Baylor College of Medicine, Houston, Texas
  • 3Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City
  • 4Digital Diagnostics, Coralville, Iowa
  • 5Iowa City Veterans Affairs Medical Center, Iowa City, Iowa
  • 6Department of Biomedical Engineering, The University of Iowa, Iowa City
  • 7Department of Electrical and Computer Engineering, The University of Iowa, Iowa City
  • 8Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, Maryland
JAMA Ophthalmol. Published online September 3, 2020. doi:10.1001/jamaophthalmol.2020.3190
Key Points

Question  Is autonomous artificial intelligence diabetic retinopathy screening more cost-effective than standard eye care screening examination performed by health care professionals?

Findings  This economic evaluation used decision analysis to model the cost-effectiveness of detecting and treating diabetic retinopathy and its sequelae among children with type 1 and type 2 diabetes and found an incremental cost-effectiveness ratio of $31 for type 1 diabetes and $95 for type 2 diabetes for each additional case of diabetic retinopathy identified compared with standard practice.

Meaning  These results suggest that when more than 23% of patients adhere to diabetic retinopathy screening recommendations, autonomous artificial intelligence screening is the preferred strategy and cost-saving for the patient and family.

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.

Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    ×