Personalizing Value in Cancer Care: The Case for Incorporating Patient Preferences Into Routine Clinical Decision Making
Bibliographic record
Abstract
Despite growing research demonstrating the potential for shared decision making (SDM) to improve health outcomes, patient preferences-including financial trade-offs-are still not routinely incorporated into health care decision making. As the US health care delivery system transitions to rewarding value-based care, the question of "value to whom?" assumes greater importance. To achieve the goals of value-based care, the patient voice must be incorporated into clinical decision making by embedding SDM as a routine part of clinical practice. Identified as a priority by the Centers for Medicare & Medicaid Services (CMS), SDM-related measures and initiatives have already been integrated into CMS' Center for Medicare and Medicaid Innovation (Innovation Center) demonstration projects (eg, the Oncology Care Model and Transforming Clinical Practice Initiative) and value-based payment programs (eg, the Merit-based Incentive Payment System, Medicare Shared Savings Program) to incentivize more proactive SDM engagement between patients and their providers. Furthermore, CMS has also integrated formal shared decision-making encounters into coverage and reimbursement policies (eg, for implantable cardioverter defibrillators), demonstrating a growing interest in SDM and its potential for eliciting and promoting the integration of patient preferences into the clinical decision-making process. In addition to increasing policy efforts to promote SDM, we need more research investments aimed at understanding how to optimize the science and practice of meaningful SDM. The current landscape and proposed road map for next steps in research, outlined in this review article, will help ensure the transition of pilots and research projects regarding the implementation of SDM into sustainable solutions.
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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 | 0.134 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.011 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".