Economic Factors Associated with AI Adoption in Oncology: Cost-Effectiveness Perceptions, Reimbursement Readiness, and Return-on-Investment Confidence Among Romanian Healthcare Professionals - Dragoș-Ciprian Negoiță; Livia Stanga; Horia Silviu Branea; Ciprian Ilie Roșca; Adrian Cosmin Ilie; Ovidiu Rosca (Healthcare, 2026)
Background and Objectives: Artificial intelligence (AI) tools promise efficiency gains in oncology, yet adoption depends on economic factors that remain under-characterized in Eastern European health systems. We quantified AI economic literacy, cost-effectiveness perceptions, reimbursement readiness, and return-on-investment (ROI) confidence among Romanian oncology professionals; we described candidate economic adoption profiles and examined whether sector was associated with the strength of the indirect association between literacy and willingness to invest via ROI confidence. Materials and Methods: A multicenter cross-sectional survey (N = 108) was conducted between September 2025 and April 2026 at “Victor Babes” University of Medicine and Pharmacy Timisoara and affiliated oncology services. Participants completed a 25-item AI Economic Literacy Index (AIELI; 0–25) plus 1–5 scales for ROI confidence, willingness to invest, perceived financial barriers, cost-effectiveness perception, and adoption intention. Analyses used Spearman correlations, multivariable logistic regression, k-means clustering, and covariate-adjusted moderated mediation with 5000 bootstrap resamples. Results: Mean age was 41.3 ± 10.7 years; 58.3% were female. AIELI was moderate (13.7 ± 4.6/25). Familiarity favored cost-effectiveness analysis (59.3%) over AI-specific reimbursement codes (16.7%). High willingness to invest occurred in 48.1% and was independently associated with higher AIELI (aOR 1.78 per +1 SD; 95% CI 1.17–2.71), higher ROI confidence (aOR 1.93; 1.24–3.02), lower perceived financial barriers (aOR 0.58; 0.37–0.91), and prior AI training (aOR 2.34; 1.13–4.86). Three exploratory profiles were identified: Cost-Conscious Adopters (n = 43), Reimbursement-Cautious (n = 37), and Budget-Constrained Skeptics (n = 28). Moderated-mediation models were consistent with a sector-conditional indirect association, largest in private clinics (β = 0.193; 95% CI 0.087–0.318) and weakest in public hospitals (β = 0.072; 0.014–0.158). Conclusions: In this exploratory cross-sectional sample, AI economic literacy and ROI confidence were associated with willingness to invest in oncology AI, interpreted as stated support for investment rather than an enacted procurement decision, since many respondents lacked formal budgetary authority. Because the design cannot establish temporal ordering, whether sector-tailored capability-building and reimbursement clarity would increase adoption remains a hypothesis for prospective evaluation.