Embedding Learning in a Learning Health Care System to Improve Clinical Practice
Bibliographic record
Abstract
PROBLEM: In an ideal learning health care system (LHS), clinicians learn from what they do and do what they learn, closing the evidence-to-practice gap. In operationalizing an LHS, great strides have been made in knowledge generation. Yet, considerable challenges remain to the broad uptake of identified best practices. To bridge the gap from generating actionable knowledge to applying that knowledge in clinical practice, and ultimately to improving outcomes, new information must be disseminated to and implemented by frontline clinicians. To date, the dissemination of this knowledge through traditional avenues has not achieved meaningful practice change quickly. APPROACH: Vanderbilt University Medical Center (VUMC) developed QuizTime, a smartphone application learning platform, to provide a mechanism for embedding workplace-based clinician learning in the LHS. QuizTime leverages spaced education and retrieval-based practice to facilitate practice change. Beginning in January 2020, clinician-researchers and educators at VUMC designed a randomized, controlled trial to test whether the QuizTime learning system influenced clinician behavior in the context of recent evidence supporting the use of balanced crystalloids rather than saline for intravenous fluid management and new regulations around opioid prescribing. OUTCOMES: Whether spaced education and retrieval-based practice influence clinician behavior and patient outcomes at the VUMC system level will be tested using the data currently being collected. NEXT STEPS: These findings will inform future directions for developing and deploying learning approaches at scale in an LHS, with the goal of closing the evidence-to-practice gap.
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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 | 0.027 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".