Evaluating An Automated Compounding Workflow Software for Safety and Efficiency: Implementation Study
Notice bibliographique
Résumé
BACKGROUND: The forms of automation available to the oncology pharmacy range from compounding robotic solutions through to combination workflow software, which can scale-up to cover the entire workflow from prescribing to administration. A solution that offers entire workflow management for oncology is desirable because (in terms of cytotoxic delivery of a regimen to a patient) the chain that starts with prescription and the assay of the patient's laboratory results and ends with administration has multiple potential safety gaps and choke points. OBJECTIVE: The aim of this study was to show how incremental change to a core compounding workflow software solution has helped an organization meet goals of improved patient safety; increasing the number of oncology treatments; improving documentation; and improving communication between oncologists, pharmacists, and nurses. We also aimed to illustrate how using this technology flow beyond the pharmacy has extended medication safety to the patient's bedside through the deployment of a connected solution for confirming and documenting right patient-right medication transactions. METHODS: A compounding workflow software solution was introduced for both preparation and documentation, with pharmacist verification of the order, gravimetric checks, and step-by-step on-screen instructions displayed in the work area for the technician. The software supported the technician during compounding by proposing the required drug vial size, diluents, and consumables. Out-of-tolerance concentrations were auto-alerted via an integrated gravimetric scale. A patient-medication label was created. Integration was undertaken between a prescribing module and the compounding module to reduce the risk of transcription errors. The deployment of wireless-connected handheld barcode scanners was then made to allow nurses to use the patient-medication label on each compounded product and to scan patient identification bands to ensure right patient-right prescription. RESULTS: Despite an increase in compounding, with a growth of 12% per annum and no increase in pharmacy headcount, we doubled our output to 14,000 medications per annum through the application of the compounding solution. The use of a handheld barcode scanning device for nurses reduced the time for medication administration from ≈6 minutes per item to 41 seconds, with a mean average saving of 5 minutes and 19 seconds per item. When calculated against our throughput of 14,000 items per annum (current production rate via pharmacy), this gives a saving of 3 hours and 24 minutes of nursing time per day, equivalent to 0.425 full-time nurses per annum. CONCLUSIONS: The addition of prescribing, compounding, and administration software solutions to our oncology medication chain has increased detection and decreased the risk of error at each stage of the process. The double-checks that the system has built in by virtue of its own systems and through the flow of control of drugs and dosages from physician to pharmacist to nurse allow it to integrate fully with our human systems of risk management.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».