Effect of PDCA Cycle Management Mode on Drug Loss in Inpatient Pharmacy
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Bibliographic record
Abstract
Objective: To assess effect of PDCA cycle management mode on drug loss in inpatient pharmacy. Methods: From January 2019 to December 2020, we collected the data from hospital work record of inpatient pharmacy each season and data of total drug loss. The valid data of scrap drugs included item name, specification, packing, quantity, wholesale price, expiry date, and scrap reason. In scrap drugs record of hospital, the inpatient pharmacy managers often record drug data from actual situation of inpatient pharmacy and documents from the drug supplier. In addition, we also collected the change of for each season, and compare the result between 2019 and 2020. Result: The results showed that the number of damaged batches reported in 2019 was significantly higher than the number reported in 2020 (122 vs 77), with a difference of 68% between them. Among the drug loss amount, the loss amount increased with the increase of the number of batches reported to be damaged, and the result of loss amount differed by 54%. In quarter records, we observed that most of the losses occurred in the first quarter and the fourth quarter, with monetary losses of around RMB 2,000 in 2020 and about RMB 3,200 in 2019. Compared with 2019 group, there is a lower amount loss (RMB 10,157.88 vs RMB 5515.14) in the amount loss caused by drug loss in 2020, and the annual reported loss in 2020 group is 54% of the annual reported loss in 2019. Further, the dollar loss for each quarter in 2020 group was lower than for each quarter in 2019. Conclusion: PDCA cycle management mode effectively reduced drug broken event, that it provided continuous improvement as the inpatient pharmacy carried out this cycle management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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