The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CONTEXT: - Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. OBJECTIVE: - To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. DATA SOURCES: - We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. CONCLUSIONS: - Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.
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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 | 0.011 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it