ReviewJCO clinical cancer informatics2025
Utilization of Lung Cancer Registries in Learning Health Systems for Health Care Improvement.
Review in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Advancing learning health care systems with best practice meetings: a mixed-methods study with insights from the Dutch Institute for Clinical Auditing.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2026Article
- Impacts of Multidisciplinary Lung Cancer Meeting Presentation in a Clinical Quality Registry.JTO clinical and research reports · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLung cancer is the leading global cause of cancer mortality with substantial evidence of inequity, disparity in process and outcomes, and unwarranted clinical variation. Over the last decades, there has been major evolution and discovery in best evidence-based practice (EBP), enhancing diagnostics, management, and the delivery of precision medicine. However, questions remain about the completeness of translation of best EBP into delivered care.
designLearning health systems (LHSs) have been defined as improvement environments where knowledge generation processes are embedded into daily clinical practice to continually improve the quality, safety, and outcomes of health care delivery. Lung cancer clinical quality registries (CQRs) provide a rigorous infrastructure supporting LHS function through the collection, analysis, and reporting of care process and outcome information delivered by health service organizations. CQRs measure the appropriateness and effectiveness of delivered care and report on the degree of best EBP delivery by stakeholder providers. The provision of risk-adjusted, benchmark reporting to stakeholders describes equity, disparity, and unwarranted clinical variation and is a fundamental driver of improvement in the safety and quality of care provided to consumers.
resultsThere is mounting international evidence of the positive impacts of CQR reporting on management processes, health care infrastructure, survival, quality improvement, and education within lung cancer communities. The use of implementation science approaches including the Knowledge to Action framework targets bridging the gaps between evidence-based knowledge and practice.
conclusionRegistry evolution is exampled by the Danish Lung Cancer Registry, National Lung Cancer Audit (United Kingdom), Dutch Lung Cancer Audit, and Victorian Lung Cancer Registry (Australia), which identify innovation opportunities to close the evidence-practice gap, overcome service deficits, and lead to better decision making for health care improvement.
Indexed as
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.