PATONCOS: A Novel Patient Stratification Tool Integrating Clinical and Economic Data for Benchmarking Oncology and Hematology Care

Background:Advances in oncology have led to the development of novel targeted therapies with demonstrated efficacy in clinical trials; however, their real-world economic impact prior to and after market introduction remains insufficiently characterized [1,2]. Cancer-related healthcare costs vary significantly depending on disease stage, time since diagnosis, tumor type, and therapeutic approach[3–6], making inter-hospital comparisons challenging due to heterogeneity in patient populations and information systems [7]. Therefore, integrating cost analysis with clinically meaningful patient stratification is essential to improve resource allocation and outcome evaluation[8–12]. Methods: A multicentre working group comprising four tertiary hospitals in Madrid (Spain) was established to develop and validate a novel classification system for adult oncohematological patients. A standardized methodology was designed to stratify patients into homogeneous groups (PATONCO categories) based on tumor location, therapeutic objective, and clinically relevant biomarkers. A cost indicator was defined as the average cost per patient per month for each PATONCO category. Data were extracted from pharmacy dispensing systems and analyzed using descriptive and inferential statistics, including Kruskal–Wallis and post hoc Dunn tests. Results: A total of 3,659 patients were included (3,168 oncology; 491 hematology), distributed across 62 programmes (54 oncology; 8 hematology). The PATONCOS tool enabled the identification and validation of a cost indicator (average cost/patient/month per category), allowing inter-hospital comparison. Significant differences in costs were observed across most high-prevalence categories, reflecting variability in therapeutic strategies and adoption of innovative treatments. The model demonstrated its capacity to detect intra-group homogeneity and inter-group variability, improving the identification of high-cost patient subgroups and supporting benchmarking across centres. Conclusions: The PATONCOS tool provides a novel, clinically oriented stratification methodology that integrates pharmacotherapy, biomarkers, and disease stage with economic evaluation. This approach enables more accurate comparisons of oncology treatment costs between institutions and supports data-driven decision-making in resource allocation. Its implementation may contribute to more sustainable healthcare systems by aligning clinical practice with economic outcomes.