What question did this study set out to answer?

This research aims to develop a clinical decision support system that optimizes costs in breast cancer care while ensuring adherence to national guidelines.

May 30, 2026

Development of a point-of-care clinical decision support system to optimize total cost of care, while preserving quality outcomes: Integration of economic, clinical, pathological, genomic data, and national guidelines.

Key Points

This research aims to develop a clinical decision support system that optimizes costs in breast cancer care while ensuring adherence to national guidelines.
Conducted a retrospective cost analysis of stage I–III breast cancer with 1-year follow-up using a CDS platform.
Integrated clinical, pathological, and genomic data with ASCO/NCCN guidelines to present treatment pathways and projected costs.
Analyzed costs using standardized Medicare fee schedules, adjusting for temporal and insurance fee biases.
Identified mean potential cost savings of 21.8% ($7,160 per patient).
Physician pathway selection shifted, leading to a 15-20% reduction in total costs for 1-year estimated care.
The CDS facilitated integration of economic insights into decision-making without compromising guideline adherence.

Abstract

e23301 Background: Rising cancer care costs challenge patients and policymakers, with approximately 80% of expenditures driven by physician decisions at the point of care. National guidelines (ASCO/NCCN) emphasize clinical efficacy but rarely incorporate economic impact. Prior work demonstrated cost variation exceeding 50, 000 among comparable cohorts of breast, colorectal, and lung cancer pts (Pecora; J Prec Med 2020). We developed an artificial intelligence–enabled clinical decision support (CDS) system that integrates clinical, pathological, and genomic data with evidence-based national guidelines and cost estimates. Methods: We conducted a retrospective cost analysis of stage I–III breast cancer using the CDS platform. The tool synthesizes multidimensional patient data and presents NCCN/ASCO-supported treatment pathways with projected, practice-specific costs. The primary endpoint was estimated cost savings, defined as the difference between 1-year total cost of care recommended by the oncologist and by the CDS. Costs reflected medical oncology only (excluding radiation/surgery) and were standardized using Medicare fee schedules, adjusted for temporal bias (2020–2022) and for insurance fee bias. Consecutive stage I–III breast cancer pts treated at a large community practice and an academic cancer center (2020–2022) were identified, with clinical data extracted from the EMR. Only complete cases with 1-year follow-up were analyzed. IRB approval was obtained with waiver of informed consent. Results: Patients were stratified into risk-adjusted cohorts by stage and prognostic/predictive factors, including genomic data. Of 2, 434 records reviewed, 1, 188 pts had complete data (579 hospital-based). The CDS identified mean potential cost savings of 21. 8% (7, 160 per pt). In a pilot intervention displaying guideline-concordant options alongside comparative costs, physician pathway selection shifted, yielding a 15–20% reduction in calculated 1-year total costs. Conclusions: Integrating economic insights into point-of-care oncology decision-making facilitates value-based care without compromising guideline adherence or quality. This approach enables cost-sharing models between payers and providers while maintaining evidence-based treatment. The CDS is currently piloted in a large multicenter oncology practice, with broader implementation underway in partnership with a major healthcare payer. Breast cancer: 1 year estimated total cost of care. Patients Initial Treatment Cost Savings Opportunity % Reduction Stage I 707 13, 795, 728. 02 3, 636, 927. 32 26. 36% Stage II 391 18, 977, 328. 88 3, 371, 674. 01 17. 77% Stage III 90 7, 066, 640. 88 1, 497, 921. 41 21. 20% Total 1188 39, 851, 243. 58 8, 506, 522. 74 21. 35%

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