L’implantation de l’approche Lean : le cas de la pharmacie de l’Hôpital Fleurimont au Centre hospitalier universitaire de Sherbrooke
Bibliographic record
Abstract
Resume Introduction : Depuis une dizaine d’annees, on constate un interet grandissant de la part des etablissements de sante pour l’approche Lean . Cet article decrit un projet d’amelioration du temps de preparation des ordonnances au departement de pharmacie de l’Hopital Fleurimont du Centre hospitalier universitaire de Sherbrooke. Description de la problematique : Alors que le personnel de la pharmacie traitait plus de 448 280 ordonnances dans le courant de l’annee 2003-2004, ce chiffre s’elevait a environ 598 135 en 2008-2009, soit une augmentation de pres de 34 % contre seulement 5 % d’effectifs supplementaires. Resolution de la problematique : Au cours de l’annee 2008, un comite de travail a ete forme a l’initiative de la direction de la pharmacie pour discuter des recommandations deposees par un consultant, dont l’une d’entre elles proposait la revision du processus de traitement des ordonnances a la pharmacie. Le projet a demarre en septembre 2008 pour se terminer officiellement en mai 2009. Le comite de travail s’est reuni a 19 occasions pour un total de 45 heures en ayant recours aux methodes et aux outils Lean (p. ex. cartographie des processus, diagrammes a ficelles, loi de Pareto et controles visuels, standardisation, cellule de travail, etc.). Des exemples et des mesures de resultats ont servi a la verification des retombees de l’usage de ces outils sur le traitement des ordonnances de medicaments. Conclusion : Dans un reseau de la sante ou les ressources se font rares, le Lean permet des gains significatifs et durables tout en reduisant les investissements. Ainsi, avec des investissements d’environ huit mille dollars, le Centre hospitalier universitaire de Sherbrooke a pu reduire de 35 % le delai de traitement des ordonnances par le personnel du departement de pharmacie de l’Hopital Fleurimont tout en ameliorant significativement le climat de travail. Abstract Introduction: Over the past 10 years, healthcare establishments have shown an increasing interest in the Lean method. This article describes a project in which prescription preparation time was improved at the Fleurimont Hospital site of the Centre hospitalier universitaire de Sherbrooke . Problem description: Although pharmacy personnel evaluated more than 448 280 prescriptions during 2003–2004, this was increased by nearly 34% to 598 135 in 2008–2009 with an increase in additional staff of only 5%. Problem resolution: In 2008, under the initiative of pharmacy management, a task force was formed to discuss the recommendations made by a consultant, one recommendation being the revision of the prescription management process at the pharmacy. The project started in September of 2008 and was officially finished in May of 2009. Using the Lean method and associated tools (process maps, flow charts, Pareto’s law and visual aids, standardization, workgroups, etc), the taskforce met 19 times for a total of 45 hours. Examples and outcome measures were used to evaluate the impact on prescription management of using these tools. Conclusion: In a healthcare system with limited resources, the Lean method allowed significant and sustainable gains while reducing investments. Also, with an investment of approximately $8000, the Centre hospitalier universitaire de Sherbrooke reduced by 35% the delay in prescription evaluation by personnel of the pharmacy department of the Fleurimont Hospital, this while significantly improving working conditions. Key words: medication circuit, distribution, Lean
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".