Defining Cancer Care Quality or Delivering Quality Cancer Care?
Bibliographic record
Abstract
Many in the oncology world see as accepted that quality and value are difficult to define and that no specialty-wide consensus is likely to be reached in the short term about how to measure and report success at improving either.Others find the definition just beyond their grasp and suggest that, like pornography, they know it when they see it.Large organizations, publications, conferences, and processes have sprung up around the concept, and various attempts have been made to develop "measures of quality" that can characterize a practice or physician in this area.This work includes ongoing efforts within and among the National Quality Forum, NCCN, ASCO, The US Oncology Network (The Network), the Community Oncology Alliance, and Ontario Cancer Care to identify quality measures-many of which focus on surrogate processes or activities that should lead to quality.Instead, however, we believe this effort and investment should focus on outcomebased measures, and we offer a paradigm for measuring interventions:• Quality is the efficient delivery of evidence-based care by trained clinicians in an accessible setting.• In this context, value is providing higher quality care at the same cost, or the same quality at a lower cost.We believe these concepts are at the core of cancer care delivery, whether in the community or academic setting and across specialties, including medical oncology, radiation oncology, gynecologic oncology, urology, and surgery.Achieving quality will require integrated, coordinated care with clinical teams spanning these professionals and settings.It requires not only coordination among clinicians of different specialties but also investment in infrastructure resources to facilitate the integration of process development and clinical decision support tools that support evidence-based practices that optimize practice efficiency and care delivery.If the right investments have been made and communications highways created, these definitions are measurable, reportable, and comparable (without significant manual effort).Just as important, these definitions allow quality and value to evolve over time as the evidence evolves.Consequently, the highest-quality treatment pathways, and even the best modalities of treatment, may be vastly different in the future.For example, advance care planning and palliative care services would likely not have been included in the concept of evidence-based care 10 years ago.Today, however, the data suggest that palliative care concurrent with disease-directed care can improve outcomes and patient experiences while reducing costs.This view is also supported by a decade of hypothesis, consensus-building, technology investment, data collection, and analysis by physicians and supporting clinicians and staff of The Network, who we believe pioneered the concepts of narrowly drawn, evidence-based pathways for care in oncology.Nearly a decade ago, physicians in The Network decided to develop Level I Pathways, evidence-based guidelines that redirect the wide range of treatments in oncology care into more precise, clinically proven treatment options.(More information is available at http://www.usoncology.com/cancercareadvocates/
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.105 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".