Getting (Along) With the Guidelines: Reconciling Patient Autonomy and Quality Improvement Through Shared Decision Making
Bibliographic record
Abstract
In past decades, stark differences in practice pattern, cost, and outcomes of care across regions with similar health demographics have prompted calls for reform. As health systems answer the growing call for accountability in the form of quality indices, while responding to increased scrutiny on practice variation in the form of pay for performance (P4P), a rift is widening between the system and individual patients. Currently, three areas are inadequately considered by P4P structures based largely on physician adherence to guidelines: diversity of patient values and preferences; time and financial burden of therapy in the context of multimorbidity; and narrow focus on quantitative measures that distract clinicians from providing optimal care. As health care reform efforts place greater emphasis on value-for-money of care delivered, they provide an opportunity to consider the other "value"-the values of each patient and care delivery that aligns with them.The inherent balance of risks and benefits in every treatment, especially those involving chronic conditions, calls for engagement of patients in decision-making processes, recognizing the diversity of preferences at the individual level. Shared decision making (SDM) is an attractive option and should be an essential component of quality health care rather than its adjunct. Four interwoven steps toward the meaningful implementation of SDM in clinical practice-embedding SDM as a health care quality measure, "real-world" evaluation of SDM effectiveness, pursuit of an SDM-favorable health system, and patient-centered medical education-are proposed to bring focus back to the beneficiary of health care accountability, the patient.
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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 | 0.168 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.011 | 0.028 |
| 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".