Reviewnpj health systems2026
Modernizing data and data science infrastructure as a strategic asset for cancer center.
Review in npj health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Leveraging electronic health record data for precision medicine insights: the precision medicine registry at NYU Langone Health.npj health systems · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern cancer centers face increasing data-driven demands from institutional, state, and federal stakeholders, as well as from patients and communities. Data now functions as a strategic asset that supports care delivery, research, education, community engagement, and policy improvement and development. However, many cancer centers continue to operate with fragmented systems, limited interoperability, and underdeveloped analytics capacity, which constrain their ability to meet expectations or drive innovation. This perspective advocates for adopting a Learning Health System - Learning Health Community (LHS-LHC) framework as a strategic approach to modernizing cancer data and data science infrastructure. We outline how cancer centers can develop integrated, interoperable, and inclusive data systems that enable continuous learning, promote equity-driven outcomes, and generate real-world impact. By embedding data and data science as core institutional infrastructure, and by aligning investments across health systems, funders, and community partners, cancer centers can enhance their role as regional anchors for coordinated, patient-centered care and lead a national transition toward data-enabled oncology.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.