Economic efficiency of diagnostics using artificial intelligence-assisted patient routing: an evaluation method

Vollständiger Abstract

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Objective: To develop and validate a method for evaluating the economic efficiency of target disease (TD) diagnostics performed via artificial intelligence (AI)-assisted multi-stage patient routing. Material and methods. The evaluation method was developed through a simulation of two diagnostic routing scenarios (with and without AI program output) based on data from 381 patients with malignant and benign skin neoplasms. This approach was validated using output of the Derma Onko Check AI program, employing previously proposed diagnostic algorithms for melanocytic skin tumors (n=230) at a 62% routing threshold. Formulas were derived to calculate the financial cost (FC) ratio, the cost of identifying one TD case, and coefficients for avoidable and potential avoidable costs to enable mapping within a quadrant matrix. The evaluation method factors in not only the avoidable costs of medical interventions but also the potential avoidable costs (losses) resulting from delayed TD detection. Results. The implementation of diagnostic algorithms based on the output of the Derma Onko Check AI program demonstrated high economic efficiency. The FC ratio of 0.49 indicates a 51% reduction in the total FCs for melanocytic skin tumors compared to conventional diagnostics. The analysis of avoidable and potential avoidable costs revealed a 59.0% decrease in avoidable costs (R TC _AC=0.41) and a 51.0% decrease in potential avoidable costs (R TC _PAC=0.49). These results fall within the optimal efficiency zone of the quadrant matrix. Conclusion. The obtained results validate factoring missed-case treatment costs into both parts of the R TC formula. Thisensures accurate comparability of diagnostic approaches with different FC structures and clinical outcomes.