Pricing models for diagnostic AI based on qualitative insights from healthcare decision makers.

Abstract

AI-enabled diagnostic decision support systems (DDSS) could improve diagnostic accuracy and efficiency, yet adoption is often impeded by pricing approaches that rely on opaque technical usage metrics. We examined how pricing can remain clinically legible and budgetable while accounting for AI-specific technical and organizational cost drivers. We conducted semi-structured interviews with healthcare decision makers (n = 17) across hospital, outpatient, laboratory, and industry settings and conducted a deductive-inductive thematic analysis. Ten themes emerged, including widespread resistance to purely usage-based pricing and strong preferences for transparency and predictability. Participants supported hybrid models combining a base fee with variable components defined in clinically meaningful units (per patient, per test, or per episode) and emphasized reimbursement alignment alongside integration, training, and support as integral value elements. Outcome-linked payment was viewed as ethically compelling but operationally difficult. We synthesize these findings into stakeholder-informed design principles and actionable recommendations for pricing models that facilitate procurement, reimbursement fit, and sustainable scaling of diagnostic AI.