Project Profile: New Computing Model Helps Hamilton Health Sciences Address Changing Business Requirements
Notice bibliographique
Résumé
This case study presents the impetus, business case, chronology and benefits of implementing a new server-based computing model at Hamilton Health Sciences that solved a critical desktop management problem while reducing IT costs.The new approach also provided a robust, flexible and scalable technology platform that is helping the hospital address business requirements driven by the emerging virtual healthcare community. Hamilton Health Sciences at a GlanceHamilton Health Sciences (HHS), which serves the more than 2.2 million residents of Hamilton, Central South and Central West Ontario, was formed through the merger of five hospitals and one cancer centre: Chedoke Hospital, Hamilton General Hospital, Henderson General Hospital, McMaster Children's Hospital, McMaster University Medical Centre, as well as the Juravinski Cancer Centre.Together, these facilities offer a range of acute and specialized services, catering to healthcare needs from preconception through to aging adults.This, in combination with its focus on academics and research, makes Hamilton Health Sciences the employer of choice for nearly 10,000 people.(www.hhsc.ca) Increasing Automation Strains IT ResourcesThe adoption of technology at HHS is now in high gear as decision-makers begin to see its full potential as a critical enabler for reducing cost, improving clinical and operational efficiency, attracting and retaining the best medical professionals and improving patient care and safety.As technology began to gain traction at HHS, however, the hospital's IT infrastructure and its Information & Communications Technology team (ICT) began to feel the strain.Growing pressure from users for more and better applications was a big challenge, for example.ICT, a group of 80 people that was already managing more than 100 servers, 5,000 PCs and 10,000 users, was facing a queue of 177 application requests.Worse yet, the demand from users to keep up with the latest versions of PC applications such as the Microsoft Office Suite, presented an almost insurmountable problem, requiring the continual upgrading of application and operating system software in 5,000 PCs, a huge, time-consuming and expensive undertaking, even with the help of advanced tools.With the growing number of users and PCs, the ICT budget would not be able to sustain this operating model. Server-Based Computing Offers Significant BenefitsRather than throwing more people and money at the escalating desktop management problem, ICT decided to look for a new computing model that would enable the group to keep desktop systems current, reduce ongoing operating and support costs, address growing requirements for user mobility and quickly, easily and cost-effectively realign the hospital's IT infrastructure to meet rapidly changing business requirements, including emerging regional, provincial and national e-health initiatives.A thorough analysis of the infrastructure supporting the hospital's current desktop implementation and a review of alternate approaches led to an investigation of how server-based computing (a.k.a."thin-client computing") could help ICT achieve these objectives.Server-based computing (SBC) simplifies the management and support of the desktop environment by moving applications and data off personal computers and onto corporate servers.While the look, feel and functionality of applications remains the same as perceived by the user, the user's PC becomes just a terminal device, passing keystrokes and mouse clicks up to, and displaying screen images sent down from, the applications running on the centralized corporate servers.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,004 |
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 ».