Cancer centers today are facing growing pressure from stakeholders across the cancer care and research community to meet data-driven expectations. Yet many centers continue to rely on fragmented, siloed data systems that limit their ability to improve care, accelerate discovery and address care gaps.
A Perspective article published in npj Health Systems calls on cancer centers to treat data and data science infrastructure as a strategic institutional asset, not simply an operational byproduct, and to modernize systems using a Learning Health System and Learning Health Community approach to support continuous improvement in cancer care.
Regenstrief Institute Chief Data Scientist Jiang Bian, Ph.D., and colleagues from Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine and others describe how connected and interoperable data ecosystems allow centers to learn from routine care and apply insights to improve outcomes.
“Modernizing data infrastructure is essential to improving how cancer centers care for patients and work with the communities they serve,” said Dr. Bian, the Walther and Regenstrief Endowed Chair in Cancer Informatics, IU Melvin and Bren Simon Comprehensive Cancer Center and Regenstrief Institute. “Cancer care generates large volumes of information, including electronic health records, imaging, genomics, patient reported outcomes and social drivers of health.”
Learning Health Systems and Learning Health Communities help health care organizations continuously improve by using data from everyday care to generate evidence and apply it back into practice.
“In a Learning Health System, we use information from electronic health records, clinical outcomes and patient experiences to continuously analyze what works best. That allows care teams to learn from every patient encounter and use those insights to improve treatment decisions and overall quality of care,” said Christina M. Scifres, M.D., Leader, Regenstrief Institute Strategic Initiative on Learning Health Systems.
“A Learning Health Community builds on that approach by extending it beyond a single institution and connecting health systems with public health agencies, community organizations, registries and other partners so knowledge can be shared and applied more broadly,” said Dr. Scifres.
Moving beyond fragmented data systems
The authors argue that modern cancer centers must invest in interoperable, inclusive data ecosystems that can support:
- Continuous learning and quality improvement
- More access to clinical trials
- Better characterization of cancer burden across populations
- Faster translation of research discoveries into practice
“Data and data science have to be treated like core infrastructure, more like a public utility than an IT byproduct,” said Dr. Bian. “When cancer centers invest in connected, interoperable data and the workforce to use it, they can turn real-world care into real-world evidence that improves decisions for patients.”
Cancer centers often cannot fully see who they reach and who they miss because patient care spans multiple systems and data remain fragmented. Without interoperable data, performance can appear stronger than it is while gaps stay hidden. Connected data systems help centers spot care delays, improve trial matching and monitor screening and outreach using real-time evidence from routine care.
By strengthening interoperable data infrastructure and learning cycles, centers can contribute more effectively to multi-institution research, public health reporting and coordinated cancer care improvement efforts nationwide.
More information
Yi Guo et al, Modernizing data and data science infrastructure as a strategic asset for cancer center, npj Health Systems (2026). DOI: 10.1038/s44401-025-00058-9
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