The development of a positive deviancy strategy to identify excellence in patient experience
Bibliographic record
Abstract
Rationale, aims and objectives: Patient experience is recognized as a key target of quality improvement to foster patient-centered care. Identification of excellence in patient experience could highlight behaviors that may be shared to improve quality. The objective of this study was to develop a strategy to identify positive practice deviants in patient experience at the surgeon level.Methods: Patient experience surveys were analyzed from 1707 discharged surgical patients. Multilevel hierarchical regression models were developed to predict topbox scores of global rating and physician communication. The influence of the surgeon outlier from the larger group was determined by identifying the random effect of the cluster and measuring the intra-class correlation (ICC).Results: In 2 services with greater than 20 surgeons, positive deviants were identified in the physician composite score (random surgeon effect <0.05). Removal of the surgeons from the analysis of the composite measure resulted in a 69% decrease in the ICC (7.54% to 2.3%) in the first service and a 32% decrease in the ICC (6.83% to 4.59%) in the second service and the random surgeon effect was no longer significant in either service (p>0.05).Conclusion: This study has illustrated the application of a process which can identify positive deviants at the provider level for the delivery of excellence in patient experience.
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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 | 0.029 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".