The Webiomed.DHRA decision-making support system for medical examinations in the Russian Federation: a cost-effectiveness analysis

ABSTRACT

Objective. Cost-effectiveness analysis of decision-making support system with artificial intelligence automating risk assessment using the SCORE scale in preventive medical examinations.

Material and methods. An original mathematical model was created to simulate 10-year survival of patients with various cardiovascular risks assessed by the SCORE system. Two scenarios were compared: (1) risk assessment by a physician with decisionmaking support system, and (2) risk assessment without decision-making support. The primary effectiveness outcome was lifeyears gained per a patient. The incremental cost-effectiveness ratio (ICER) was calculated to assess economic feasibility. Analysis was conducted from the healthcare system perspective. We considered the following costs: implementation of decision-making support system, physician visits (screening, diagnosis, treatment assignment, follow-up), and pharmacotherapy for patients entitled to medication benefits. The target population was adults aged 40—65 without CVD, type 2 diabetes mellitus, or chronic kidney disease. Clinical efficacy in the model corresponded to efficacy of the Webiomed.DHRA decision-making support system that was expressed in accuracy of determining the CVR (“correctness”) according to the SCORE scale.

Results. The modeled 10-year mortality was 1.25% in scenario 1 and 3.70% in scenario 2 (OR 0.33, 95% CI: 0.27—0.40). Thus, there was significantly lower mortality in case of decision-making support system. Mean undiscounted annual cost per patient was RUB 7,947.97 (discounted: RUB 6,530) and RUB 4,444 (discounted: RUB 3,683), respectively. The ICER was RUB 276,905 per life-year gained without discounting and RUB 224,983 with discounting. This value is lower than cost-effectiveness threshold for 2024. The majority of costs (87.61%) were attributed to medical visits, while implementation of decision-making support system accounted for only 0.051%.

Conclusion. Introduction of decision-making support system with artificial intelligence for assessing cardiovascular risks into regional healthcare system will reduce mortality over the next 10 years after medical examination. According to cost-effectivenessanalysis, this system is economically acceptable medical technology.