12: Lean Management and Just-in-Time Ordering Reduces Palivizumab Wastage in a Provincial RSV Prophylaxis Program
Bibliographic record
Abstract
Immunoprophylaxis with palivizumab is recommended for infants at high risk of RSV-related hospitalization. Palivizumab is an expensive sole-sourced biologic pharmaceutical available in single dose vials which expires 6 h upon reconstitution, resulting in potentially high rates of drug wastage. Palivizumab has a short shelf life and requires cold-chain storage making left-over inventory costly and undesirable. Principles of Lean production and Just-In-Time production can be applied to minimize wastage and costs. To describe the outcome of Lean management and Just-In-Time inventory control in the operations of a provincial RSV immunoprophylaxis program. The Manitoba RSV Immunoprophylaxis Program (MB RSVP) coordinates the use of palivizumab for eligible patients in the province of Manitoba, Canada, a region of 650,000 square km with population of 1.27 million. Yearly PDCA cycles using Lean management principles have resulted in a centralized provincial coordinating centre that manages RSV immunoprophylaxis including patient enrolment, coordination of injection through a network of health care providers (to allow for cohorting and vial sharing), just-in-time ordering and shipping of palivizumab to clinic sites, and continuious inventory tracking. Dosing reports from sites function as a Kanban system triggering future shipment of palivizumab. Regular discussion with all involved in the Program fosters involvement, problem solving, and continuous improvement. The overall objective is limitation of wastage while maximizing patient benefit. Over three seasons (2011–2014), the MB RSVP enrolled and organized RSV immunoprophylaxis for 868 patients with over 3500 doses provided. The Program was effective in limiting wastage of palivizumab (mg of drug ordered but not given to patient) to 10% (range 8.4% to 12.8% per year). Just-in-time ordering reduced left over inventory from 157 vials (in 2012) to 75 vials (in 2014) resulting in a decreased inventory carrying cost from $118,000 to $56,000 Canadian respectively ($752/50 mg). Using Lean business management principles and Just-In-Time inventory control resulted in minimum wastage of palivizumab and decreased yearly left-over inventory of palivizumab in a provincial RSV prophylaxis program.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".