Reducing Unnecessary Phlebotomy Testing Using a Clinical Decision Support System
Bibliographic record
Abstract
INTRODUCTION: Reducing unnecessary tests reduces costs without compromising quality. We report here the effectiveness of a clinical decision support system (CDSS) on reducing unnecessary type and screen tests and describe, estimated costs, and unnecessary provider ordering. METHODS: We used a pretest posttest design to examine unnecessary type and screen tests 3 months before and after CDSS implementation in a large academic medical center. The clinical decision support system appears when the test order is initiated and indicates when the last test was ordered and expires. Cost savings was estimated using time-driven activity-based costing. Provider ordering before and after the CDSS was described. RESULTS: There were 26,206 preintervention and 25,053 postintervention specimens. Significantly fewer unnecessary type and screen tests were ordered after the intervention (12.3%, n = 3,073) than before (14.1%, n = 3,691; p < .001) representing a 12.8% overall reduction and producing an estimated yearly savings of $142,612. Physicians had the largest weighted percentage of unnecessary orders (31.5%) followed by physician assistants (28.5%) and advanced practice nurses (11.9%). CONCLUSIONS: The CDSS reduced unnecessary type and screen tests and annual costs. Additional interventions directed at providers are recommended. The clinical decision support system can be used to guide all providers to make judicious decisions at the time of care.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".