Five-Year Cost-Effectiveness Modeling of Primary Care-Based, Nonmydriatic Automated Retinal Image Analysis Screening Among Low-Income Patients with Diabetes

Spencer D. Fuller, Jenny Hu, James C. Liu, Ella Gibson, Martin Gregory, Jessica Kuo, Rithwick Rajagopal

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Artificial intelligence-based technology systems offer an alternative solution for diabetic retinopathy (DR) screening compared with standard, in-office dilated eye examinations. We performed a cost-effectiveness analysis of Automated Retinal Image Analysis System (ARIAS)-based DR screening in a primary care medicine clinic that serves a low-income patient population. Methods: A model-based, cost-effectiveness analysis of two DR screening systems was created utilizing data from a recent study comparing adherence rates to follow-up eye care among adults ages 18 or older with a clinical diagnosis of diabetes. In the study, the patients were prescreened with an ARIAS-based, nonmydriatic (undilated), point-of-care tool in the primary care setting and were compared with patients with diabetes who were referred for dilated retinal screening without prescreening, as is the current standard of care. Using a Markov model with microsimulation resulting in a total of 600 000 simulated patient experiences, we calculated the incremental cost-utility ratio (ICUR) of the two screening approaches, with regard to five-year cost-effectiveness of DR screening and treatment of vision-threatening DR. Results: At five years, ARIAS-based screening showed similar utility as the standard of care screening systems. However, ARIAS reduced costs by 23.3%, with an ICUR of $258 721.81 comparing the current practice to ARIAS. Conclusions: Primary care-based ARIAS DR screening is cost-effective when compared with standard of care screening methods.

Original languageEnglish
JournalJournal of Diabetes Science and Technology
DOIs
StateAccepted/In press - 2020

Keywords

  • artificial intelligence
  • cost-effectiveness analysis
  • diabetic retinopathy
  • healthcare economics
  • machine learning technology
  • public health

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