Improving Clinical Practice Using a Novel Engagement Approach: Measurement, Benchmarking and Feedback, A Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Poor clinical outcomes are caused by multiple factors such as disease progression, patient behavior, and structural elements of care. One other important factor that affects outcome is the quality of care delivered by a provider at the bedside. Guidelines and pathways have been developed with the promise of advancing evidence-based practice. Yet, these alone have shown mixed results or fallen short in increasing adherence to quality of care. Thus, effective, novel tools are required for sustainable practice change and raising the quality of care. METHODS: The study focused on benchmarking and measuring variation and improving care quality for common types of breast cancer at four sites across the United States, using a set of 12 Clinical Performance and Value(®) (CPV(®)) vignettes per site. The vignettes simulated online cases that replicate a typical visit by a patient as the tool to engage breast cancer providers and to identify and assess variation in adherence to evidence-based practice guidelines and pathways. RESULTS: Following multiple rounds of CPV measurement, benchmarking and feedback, we found that scores had increased significantly between the baseline round and the final round (P < 0.001) overall and for all domains. By round 4 of the study, the overall score increased by 14% (P < 0.001), and the diagnosis with treatment plan domain had an increase of 12% (P < 0.001) versus baseline. CONCLUSION: We found that serially engaging breast cancer providers with a validated clinical practice engagement and measurement tool, the CPVs, markedly increased quality scores and adherence to clinical guidelines in the simulated patients. CPVs were able to measure differences in clinical skill improvement and detect how fast improvements were made.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".