Effect of PDCA Cycle Management Mode on Drug Loss in Inpatient Pharmacy
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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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".