An Exploratory Assessment of IT Management Issues in Ontario Hospitals
Notice bibliographique
Résumé
Background and context: Given the constant evolving developments in information technology (IT) in healthcare in Canada and Ontario, and the relatively nonexistent body of literature on IT management issues from the perspectives of top IT managers (i.e. Chief Information Officers, IT directors, IT top managers) in hospitals, a follow up study of IT management issues to the study done by Jaana et al. is conducted. Purpose: To develop an authoritative list of IT management issues in Ontario hospitals and compare the results to the earlier study and the literature. Methods: Using the Ranking Type Delphi technique, the responses from IT top managers in three main panels of Ontario hospitals were solicited through a controlled iterative feedback process. The hospitals were divided into the academic panel (n = 6), community panel (n = 12), and the rural panel (n = 8) for a total of 26 out of 33 participants who completed the study. Results: 26 issues were raised and a total of 24 issues were ranked in the study. Among the 14 common issues between the three panels, the top five issues were limited funding, keeping infrastructure current, external security threats, increasing cost, and managing demands for IT projects. Comparing with the study by Jaana et al. (2011), a total of 7 new issues emerged which are concerned with technology, regulatory challenges, and human issues. A total of 10 issues were dropped from the earlier study spanning areas of strategic, technological, organizational, and human issues. The participants in the study did not significantly differ individually based on their background characteristics, where the only significant difference observed between the hospital panels was due to hospital characteristics. During the brainstorming phase a total of 195 issues were provided which were consolidated by two researchers to form a list of 26 IT management issues, with an inter coder reliability of 88%. The issues with a 4.5 out of 7 rating and higher on a Likert scale were retained to narrow down the list. This resulted in 19 issues for the rural and community panels, and 21 issues for the academic panel, with 14 of the 26 issues being common to all three panels. The ranking phase was conducted with two rounds of ranking due to the low consensus levels during the first round. The consensus level after two rounds was; W academic = 0.235, W community = 0.254, and W rural = 0.381. Contributions: This study presents a significant contribution to the management of medical informatics field by providing an approach to categorize IT management issues to observe trends overtime as well as present the application of a seminal framework to explain the changes in these issues as organizations change and grow overtime. At the management and practical levels, the list of prioritized issues provides an evidence base for top IT managers to make IT related decisions at the organizational level. The list also acts as a second benchmarking tool to evaluate hospital performance overtime with the various issues. At the policy development level, provincial governments can use the list to devise comprehensive IT management strategies to address the various regulatory issues reported. Future research can focus on exploring the resonating behind the rankings provided and replicating this study over time and across various geographies so that a large survey can be developed to follow the evolutions of IT management issues in healthcare over time.
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 | 0,008 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,010 | 0,003 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».