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Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost
Livio Di Matteo1, Noelle Whitestone2, Vishaal Bhambhwani3,4
1Lakehead University, Thunder Bay, Ontario, Canada.
Objective:
Cost analysis of autonomous artificial intelligence (AI)-based screening of diabetic retinopathy (DR) for adults with diabetes at a primary care clinic.
Methods And Analysis:
This study provides a comparative cost analysis of actual results using AI-based DR screening with counterfactual results based on all patients going through the physician-based referral system. A cost analysis is conducted using cost data from published sources, provincial billing codes, statistical sources, and patient characteristics from a clinical study to compare autonomous AI-based screening for DR versus physician-based screening. Costs considered include direct costs of operating the AI system, physician fees, and indirect costs to patient time. Along with total cost comparisons, a cost per DR case detected is estimated and a sensitivity analysis based on variations in AI costs is provided.
Results:
Over the study period, 202 participants were screened for DR using autonomous AI. The majority (93.6%, n=189) of AI-based DR screening exams were completed successfully. The AI-based scenario results in total direct costs of $C7919.04 and indirect costs of $C5728.80, resulting in total costs of $C13 647.84 per 100 patients. The traditional physician-based approach results in total direct costs of $C8240 and indirect costs of $C19 998.09, resulting in total costs of $C28 238.09 for 100 patients. When costs are converted to costs per unit outcome, the total cost per diagnosed DR case is $C620.36 for the AI-based approach and $C1283.55 for the physician-based approach; the AI-based cost per diagnosed case was 52% lower.
Conclusion:
Given the lower cost per diagnosed case of the AI-based approach, there are advantages to the implementation of AI-based screening for DR.
