Medical stewardship: Pathology evidence based ordering to reduce inappropriate test ordering in a teaching hospital
Bibliographic record
Abstract
Objective: This study was designed as an educational program aimed at promoting evidence-based pathology ordering with the aim of reducing inappropriate test ordering.Methods: Researchers benchmarked the hospital’s pathology tests ordered in 2013-2014 before conducting a multifaceted education program in 2014-2015. The intervention consisted of main priorities including pathology test auditing, in-services and lectures, development and implementation of investigation pathways, and policy and procedure compliance. The main outcome measures was a reduction in commonly inappropriate ordered pathology testing leading to a reduction in the average test per hospital admission, and a reduction in specimen collection errors.Results: Through this educational method the researchers achieved a reduction in the average test per admission in 2014-2015 (M = 12.98) from 2013-2014 (M = 13.83). A two sample t-test indicated that this difference was significant, t(3.3006) = 0.0071, p = .01. The intervention included a focus on specimen collection errors and achieved a reduction in specimen error rates (M = 2,695) from the previous year (M = 3,000). A one sample t-test indicated that this difference was significant, t(3.0804) = 0.0105, p = .05. This intervention decreased commonly inappropriate pathology requests of Full Blood Count (FBC, -4.21%), Liver Function Tests (LFTs, -8.36%), Vitamin B12 (B12, -6.45%) and Coagulation profile (-21.22%). Commonly inappropriate pathology tests decreased (M = 7,120.33) from (M = 7,609.67). A two sample t-test indicated that this difference was significant, t(3.7730) = 0.0031, p = .005.Conclusions: Results confirmed that a multi-faceted education program can reduce inappropriate pathology test ordering, commonly over-ordered pathology test ordering, and pathology specimen error rates while maintaining positive patientoutcomes.
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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 | 0.102 | 0.284 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".