What does this research mean for the field?

Achieving better quality in oncology care requires returning to bottom-up, physician-led quality improvement initiatives while leveraging AI and data-sharing technologies, rather than relying on externally imposed payment models. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To explore the progress and ongoing challenges in delivering high-quality cancer care over the past 25 years.

June 10, 2026

Review Topic: Don't Cancer Doctors Offer Good-Quality Care?

Key Points

To explore the progress and ongoing challenges in delivering high-quality cancer care over the past 25 years.
Review of historical and contemporary quality improvement efforts in oncology care.
Analysis of frameworks like Donabedian's structure-process-outcome and Porter's value equation.
Discussion of initiatives such as QOPI, Medicare payment reforms, and AI applications in clinical settings.
Despite efforts, Medicare payment reforms showed minimal improvements in quality measures.
Private payer initiatives on clinical pathways largely failed to reduce healthcare costs.
The use of AI and data-sharing is proposed as a way to improve quality measurement in cancer care.

Abstract

We offer the perspectives of two veterans on the last quarter century of quality improvement efforts in oncology care. We believe that our colleagues deliver high quality care, but challenges remain in demonstrating this assertion to patients, payers, policy makers and to our colleagues. The journey began with the American College of Surgeons (ACOS) establishing minimum standards for cancer surgery in 1919 and evolved through state cancer registries, culminating in the Surveillance, Epidemiology, and End Results (SEER) program. Donabedian's structure-process-outcome framework and Porter's value equation provided conceptual frameworks for action. ASCO developed the Quality Oncology Practice Initiative (QOPI) in 2002. Physician-led self-assessment and chart abstraction could drive improvement. QOPI expanded to include certification programs and safety standards. Integrating quality measurement and improvement with payment incentives has proven challenging. Medicare's successive payment reform attempts—from Resource-Based Relative Value (RBRVS) through the Oncology Care Model (OCM) to the Enhancing Oncology Model—have shown mixed results, with minimal to no improvements in quality measures and limited cost-savings. Private payer initiatives based on clinical pathways largely failed to reduce costs and have been scaled back. Contemporary challenges include vertical integration of health care systems without clear quality benefits, rising drug costs, physician burnout, and erosion of public trust. New tools and iterations offer hope: artificial intelligence (AI)–powered clinical documentation and analysis could dramatically reduce administrative burdens, cross-platform electronic record sharing should enable longitudinal quality analysis, and restoration of patient agency through market forces and social media may better align quality incentives. We posit that better future results require returning to bottom-up, physician-led quality improvement while leveraging AI and data-sharing technologies to make quality measurement a natural component of cancer care rather than an externally imposed burden.

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