Adoption of a Postoperative Pain Self-Report Tool: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: With electronic technologies, patients are provided with tools to easily acquire information and to manage and record their own health status. eHealth interventions are already broadly applied to perioperative care. In a similar way, we aimed to utilize a smartphone application to enable postoperative patients to partially self-manage their postoperative pain. The results of a previously performed proof-of-concept study regarding the application were promising, and nurses as well as patients were optimistic regarding this innovative mobile application. Nevertheless, in reality, it appears that the usage and overall implementation of this application have stagnated since its introduction. Problems with innovation adoption are not novel; various studies have been conducted to explore the reasons for low implementation success of eHealth applications and indicated that adoption is influenced by multiple organizational factors. This study investigated the influence of these organizational factors on the adoption process, aiming to provide more insight in the dos and don'ts for implementing eHealth in the working processes of hospital care. OBJECTIVE: This study aimed to provide insight in how to successfully implement a technological eHealth innovation in a general nonacademic hospital. METHODS: A qualitative study was conducted to explore organizational factors affecting the innovation adoption process. Data were collected by conducting semistructured one-on-one interviews with 11 stakeholders. The data were analyzed using thematic analysis identifying overarching themes. RESULTS: Absorptive capacity, referred to as an organization's dynamic capability pertaining to knowledge creation and utilization that enhances an organization's ability to gain and sustain a competitive advantage, was regarded as the most influential factor on the application's adoption. Accordingly, it appeared that innovation adoption is mainly determined by the capability and willingness to assimilate and transform new information into productive use and the ability to absorb a novel innovation. Absorptive capacity was found to be influenced by the innovation's benefit and the sense of ownership and responsibility. Organizational readiness and management support were also regarded as essential since absorptive capacity seemed to be mediated by these factors. The size of the hospital influenced eHealth adoption by the amount of resources available and by its organizational structure. CONCLUSIONS: In conclusion, absorptive capacity is essential for eHealth adoption, and it is mediated by management support and organizational readiness. It is recommended to increase the degree of willingness and ability to adopt an eHealth innovation by enhancing the relevance, engaging stakeholders, and assigning appropriate leaders to offer guidance.
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 | 0,024 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».