Barriers and Facilitators to Utilize Digital Technologies in Transitional Care: Insights from Multisite co-design sessions
Notice bibliographique
Résumé
Background: Digital tools hold promise for enhancing transitional care by improving information flow, enabling risk stratification, and assisting clinicians in decision-making. Additionally, digital tools can identify overlooked issues and provide continuous monitoring, reducing the bias inherent in assessments based on single time points(Andreu-Perez et al., 205). However, the introduction of new technologies must be approached with caution, as they can impact clinicians' roles and workforce dynamics. This study explores barriers and facilitators to utilizing technological tools for decision support in transitional care, comparing experiences between healthcare professionals in Canada and Greece. Approach: This study represents the first phase of a broader protocol aimed at assessing the feasibility of using digital tools to inform care transitions (Petsani et al., 2022). Data collection was conducted at two sites: the CRIR - BRILLIANT, McGill University, Canada, and Hippokration General Hospital in Thessaloniki, Greece. In Canada, three consultations were conducted with 8 participants, including researchers, research member, 3 rehabilitation administrators, and 3 patient representatives. In Greece, one consultation was conducted with 6 participants comprising 3 nurses, cardiologist, and 2 internal medicine residents. Two independent researchers carried out content analysis for the sessions conducted in both Canada and Greece. Conventional content analysis was chosen due to its suitability for studies aimed at describing phenomena. The results were cross-analyzed to identify common themes and differences. Results: Our results indicate several common themes from both sites, such as: ) Resources: emphasized the need for adequate resources. This encompasses robust infrastructure, including technological systems, organizational structures, and sufficient human resources. 2) Workflow Rigidity: highlighted workflow rigidity as a significant challenge in transitional care. This rigidity hinders the adoption and effective implementation of new technologies. 3) Patient and Family Engagement and Empowerment: it is an important issue for the successful adoption of digital solutions in transitional care. Comprehensive education on digital tools prepares patients and caregivers to utilize these technologies effectively. 4) Access to Data: Canadian and Greek participants identified challenges in transitional care related to timely access to support and comprehensive data utilization. Fragmented or incomplete data can hinder effective care and decision-making processes.Our analysis revealed also some main differences. Although the need for resources came out in both countries, in Canada the focus was on leveraging advanced technological infrastructures such as patient and clinician portals, wearables, and ePROMs systems while Greek participants highlighted more fundamental issues, such as the lack of doctors, nurses, and hospital infrastructures. Also, the nature of workflow rigidity differed between the two countries. In Canada, the concern centered on certain workflows being too rigid, limiting clinicians' ability to make necessary adjustments. Greek participants noted that care facilities remain inflexible in the face of technological enhancements, suggesting a broader systemic rigidity in Greece. Implications: This study lays the groundwork for implementing digital tools in care transition decision support. Insights from healthcare professionals help identify barriers and facilitators, crucial for designing effective solutions. Recognizing potential differences informs the design of multisite studies, aiding in interpreting implementation acceptance variations. Andreu-Perez, J., Leff, D. R., Ip, H. M. D.,; Yang, G.-Z. (205). From Wearable Sensors to Smart Implants-Toward Pervasive and Personalized Healthcare. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 62(2). https://doi.org/0.09/TBME.205.242275Petsani, D., Ahmed, S., Petronikolou, V., Kehayia, E., Alastalo, M., Santonen, T., Merino-Barbancho, B., Cea, G., Segkouli, S., Stavropoulos, T. G., Billis, A., Doumas, M., Almeida, R., Nagy, E., Broeckx, L., Bamidis, P., Konstantinidis, E. (2022). Digital Biomarkers for Supporting Transitional Care Decisions: Protocol for a Transnational Feasibility Study. JMIR Research Protocols, (). https://doi.org/0.296/34573
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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 | 0,029 | 0,045 |
| 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 | 0,007 | 0,005 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».