Development of the CARES model: a conceptual approach for responsive and equitable systems in cancer care delivery collaboratives

Despite advances in treatment, patients continue to experience wide variations in cancer care quality, access, and outcomes. These disparities often stem from fragmented delivery and the lack of structured, system-level quality improvement (QI) frameworks. Conceptual models can help institutions align efforts, reduce inequities, and foster collaboration. To address this gap, we established the Illinois Cancer Collaborative (ILCC), a statewide multidisciplinary initiative, and developed a QI model to promote standardization, cross-institutional learning, and enhanced care across the cancer continuum. We conducted iterative small-group interviews with oncology care professionals—including clinicians, administrators, and QI specialists—to identify gaps in cancer care delivery. Using their input and evidence-based frameworks, we developed a conceptual model for quality cancer care. Expert reviewers refined the model for use across six stages: prevention, screening, diagnosis, treatment, survivorship, and end-of-life care. The Conceptual Approach for Responsive and Equitable Systems (CARES) model emphasizes patient engagement, care coordination, and outcome evaluation across the six stages. It supports continuous learning through systematic data collection, benchmarking, and Plan-Do-Study-Act cycles. By tracking patient-centered outcomes (e.g., symptom burden, recurrence, quality of life) and system-level metrics (e.g., efficiency, safety, cost), CARES enables institutions to refine practices, streamline workflows, and achieve improvements such as adherence to guideline-concordant treatment or reduced delays in diagnostic workup. The CARES model provides a scalable framework for cancer care quality improvement. Through collaboration, data-driven decision-making, and continuous learning, it offers actionable guidance for implementation. Future efforts should evaluate its effectiveness in improving outcomes like timeliness, coordination, and patient quality of life, and assess adoption feasibility across diverse healthcare settings.