Navigating digital health solutions across organizations in the context of care transitions
Bibliographic record
Abstract
Background: Integrated care solutions often require the use of digital tools to support information sharing, communication, collaboration, and patient engagement. Implementing digital tools at different organizations and settings require thoughtfully considering how novel tools may interact with existing technology and workflows. Using a multi-site digitally enabled care transition intervention as a case example, we present an approach of how to navigate this complexity by understanding local priorities and contexts, and aligning to shared values. Approach: The Digital Bridge project (,2) aims to co-design, implement, and evaluate a multi-site digitally enabled care transition intervention from hospital to home for older adults with complex care needs. We co-designed an intervention that would bridge communication gaps between patients and clinicians, between hospital and community clinicians, and to promote patient engagement through goal-oriented care processes. The co-design working groups included people with lived experiences and hospital and primary care clinicians. The intervention targets the Acute Medicine and Rehab units at two large distinct healthcare organizations in the Greater Toronto Area with different electronic health record (EHR) systems and technology environments. We formed implementation teams at each organization comprised of administrators, clinical leaders (with primary care representation), and technology leaders. The research and local implementation teams collaborated on how to operationalize the core functionalities surfaced through the co-design process using the most appropriate technology in each organization context and environment. We gathered insights about local priorities and challenges at both the organizational and unit levels from clinical and technology perspectives by ) attending established committee meetings and 2) setting up strategic meetings with key partners. We then 3) aligned these insights with learnings from the co-design process. We 4) iteratively engaged with the implementation teams (including senior leaders) and co-design teams to ensure continued alignment between the co-designed solution, and local clinical and technical priorities. Results: This approach yielded significantly different implementation strategies at the two organizations. In one organization, after consideration of current gaps felt by clinical teams, historical context, and technology renewal plans, the co-designed intervention will be actualized by adopting a third-party vendor solution with limited integration with the organization EHR systems. In contrast, the other organization considered the on-going quality improvement effort in their model of care and the desire to improve adoption and optimal use of the existing EHR, the co-designed solution will be deployed leveraging advanced functionalities within the EHR system. Despite the different implementation strategies, both solutions will address the same challenges with functionalities surfaced during co-design with the appropriate technology and workflow in the local contexts. Implications: We demonstrated an approach of how to operationalize a technology-enabled care transition intervention differently at two organizations based on local contexts while preserving the delivery of core functionalities viewed as essential to the intervention. The evaluation phase of the Digital Bridge project will produce further insights into the effectiveness of the intervention and the tailored implementation strategies. Having an adaptive approach to technology implementation may advance the scaling of digital health interventions for integrated care across organizations and settings. References:. Steele Gray C, Tang T, Armas A, Backo-Shannon M, Harvey S, Kuluski K, Loganathan M, Nie JX, Petrie J, Ramsay T, Reid R, Thavorn K, Upshur R, Wodchis WP, Nelson M. Building a Digital Bridge to Support Patient-Centered Care Transitions From Hospital to Home for Older Adults With Complex Care Needs: Protocol for a Co-Design, Implementation, and Evaluation Study. JMIR Res Protoc. 2020 Nov 25;9():e20220.2. http://www.digitalbridgetohome.com
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".