Reducing waste: a guidelines-based approach to reducing inappropriate vitamin D and TSH testing in the inpatient rehabilitation setting
Bibliographic record
Abstract
Background: Laboratory overutilisation increases healthcare costs, and can lead to overdiagnosis, overtreatment and negative health outcomes. Discipline-specific guidelines do not support routine testing for Vitamin D and thyroid-stimulating hormone (TSH) in the inpatient rehabilitation setting, yet 94% of patients had Vitamin D and TSH tests on admission to inpatient rehabilitation at our institution. Our objective was to reduce Vitamin D and TSH testing by 25% on admission to inpatient Stroke, Spinal Cord Injury, Acquired Brain Injury and Amputee Rehabilitation units. Methods: A fishbone framework for root cause analysis revealed potential causes underlying overutilisation of Vitamin D and TSH testing. A series of Plan-Do-Study-Act (PDSA) cycles were introduced to target remediable factors, starting with an academic detailing intervention with key stakeholders that reviewed applicable clinical guidelines for each patient care discipline and the rationale for reducing admission testing. Simultaneously, computerised clinical decision support (CCDS) limited Vitamin D testing to specific criteria. Audit and feedback were used in a subsequent PDSA cycle. Frequency of Vitamin D and TSH testing on admission was the primary outcome measure. The number of electronic admission order caresets containing automatic Vitamin D and/or TSH orders before and after the interventions was the process measure. Rate of Vitamin D supplementation and changes in thyroid-related medication were the balancing measures. Results: After implementation, 2.9% of patients had admission Vitamin D testing (97% relative reduction) and 53% of patients had admission TSH testing (43% relative reduction). Admission order caresets with prepopulated Vitamin D and TSH orders decreased from 100% (n=6) to 0%. The interventions were successful; similar to previous literature, CCDS was more effective than education and audit and feedback interventions alone. The interventions represent >$9000 annualised savings.
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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 | 0.058 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".