Notice bibliographique
Résumé
A healthcare facility in Milwaukee, WI was in the final stages of preparation to launch an electronic medical record (EMR) initiative involving mobile computing workstations. Much earlier in the project, a software issue forced the mobile workstations to sit idly in storage for almost two years. When the time came to launch the project, none of the 42 workstations started. After troubleshooting, the healthcare technology management (HTM) staff determined that the batteries within the carts were “faulty.” Further investigation revealed that the batteries were not properly maintained during the two years they were stored. The clinical engineers went back to the original equipment manufacturer (OEM), but were told the batteries were out of warranty and would need to be replaced. The replacement cost totaled almost $28,000. Already under a tight budget, they were not going to be able to afford the huge cost. They were left with a hard decision—figure out where else to cut costs in order to come up with the additional $28,000 or delay the project even further until the budget was available. If the staff had identified that the batteries were nickel metal hydride (NiMH), they would have had been aware of the high rate of self-discharge and known that they required maintenance during storage. This knowledge could have saved the facility time and money. As demonstrated in this case, HTM departments cannot afford unexpected costs and delays associated with product failures due to improper maintenance and storage. Every year, departments are asked to do more with a smaller budget and fewer resources, making it imperative to find ways to save money and time. A commonly overlooked source of savings is proper battery maintenance. The benefits of proper maintenance include prolonged battery life, which can extend the replacement interval, and overall higher peak performance for longer periods of time. To maximize battery efficiency and performance, proper battery identification is paramount. Identifying battery chemistry, application, and proper maintenance will ensure a long and productive lifecycle. Lastly, coupling this knowledge with the proper battery charger or analyzer will help any department turn concept into tangible savings. About the Author
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,008 |
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 ».