Decreasing daily blood work in hospitals: What works and what doesn't
Bibliographic record
Abstract
Recurrent, inappropriate laboratory testing is a costly and wasteful use of healthcare resources. Recognizing this problem, the American Board of Internal Medicine, Canadian Society of Internal Medicine, and the Canadian Association of Pathologist all supported the Choosing Wisely campaign to reduce laboratory investigations in patients who demonstrate clinical and laboratory stability. In this narrative, we review studies looking at a variety of approaches to reduce excessive testing including education, audit and feedback, computerized physician order entry system changes, and forcing functions. Each type of intervention has its own unique advantages and disadvantages, varying in complexity, disruptiveness, effectiveness, and sustainability. Before implementing any quality improvement project, it is important to analyze the local context to identify the root causes for the practice behavior and aim to use the minimal amount of intervention to achieve the desired result. Change is often incremental and will seldom occur with a single intervention or Plan-Do-Study-Act cycle. Garnering the support of opinion leaders and a quality improvement team will help make the process and intervention a success.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".