Cost-effectiveness and cost-utility of community-based blinding fundus diseases screening with artificial intelligence: A modelling study from Shanghai, China

Abstract

Background

With application of artificial intelligence (AI) in the disease screening, process reengineering occurred simultaneously. Whether process reengineering deserves special emphasis in AI implementation in the community-based blinding fundus diseases screening is not clear.

Method

Cost-effectiveness and cost-utility analyses were performed employing decision-analytic Markov models. A hypothetical cohort of community residents was followed in the model over a period of 30 1-year Markov cycles, starting from the age of 60. The simulated cohort was based on work data of the Shanghai Digital Eye Disease Screening program (SDEDS). Three scenarios were compared: centralized screening with manual grading-based telemedicine systems (Scenario 1), centralized screening with an AI-assisted screening system (Scenario 2), and process reengineered screening with an AI-assisted screening system (Scenario 3). The main outcomes were incremental cost-effectiveness ratio (ICER) and incremental cost-utility ratio (ICUR).

Results

Compared with Scenario 1, Scenario 2 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $ 490010.62 per 10,000 people screened, with an ICER of $2619.98 per year of blindness avoided and an ICUR of $4589.13 per QALY. Compared with Scenario 1, Scenario 3 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $242313.23 per 10,000 people screened, with an ICER of $1295.60 per year of blindness avoided and an ICUR of $2269.35 per QALY. Although Scenario 2 and 3 could be considered cost-effective, the screening cost of Scenario 3 was 27.6 % and the total cost was 1.1 % lower, with the same expected effectiveness and utility. The probabilistic sensitivity analyses show that Scenario 3 dominated 69.1 % and 70.3 % of simulations under one and three times the local GDP per capita thresholds.

Conclusions

AI can improve the cost-effectiveness and cost-utility of screenings, especially when process reengineering is performed. Therefore, process reengineering is strongly recommended when AI is implemented.