Barriers and Facilitators to Utilize Digital Technologies in Transitional Care: Insights from Multisite co-design sessions
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".