A realist review of factors critical for the implementation of eHealth in chronic disease management
Bibliographic record
Abstract
Abstract Background In Canada, chronic disease is responsible for 88% of deaths and $120 billion in cost each year. With 44% of Canadian adults living with at least one chronic condition, only 66% receive necessary care. Ehealth interventions are instrumental in chronic disease management (CDM), especially since the pandemic, as they provide accessible, cost-effective solutions for self-management. Despite its promise and accelerated use, its implementation remains challenging. This paper reports on a realist review of critical factors for the implementation of eHealth interventions relevant to conditions such as heart failure, chronic obstructive pulmonary disease, chronic kidney disease, and diabetes. The findings are presented in terms of context, mechanisms, and outcomes. Methods A realist review of the primary literature was conducted by searching five databases: Medline, Embase, Cochrane, CINALH and PsycInfo. The initial search was run for a date spanning from the databases’ inception to September 2018 and subsequently updated to dates spanning from October 2018 to May 2022. A systematic and iterative approach to data extraction was used. Thematic analysis was used to identify context-mechanism-outcome (CMO) configurations. Results Among the 13,209 citations retrieved, 64 articles were included. This paper reports the top ten configurations found to facilitate or hinder eHealth implementation. Key themes related to context, such as team-based care, and action, including program use, perceived usefulness and motivation, are reported. Conclusions This study explores the role of context, mechanisms, and outcomes in ehealth implementation, highlighting the nonlinear relationships between these factors. Future implications include empirical testing CMOs as middle-range theories in real-world settings to determine causality. Trial registration The review protocol was registered with PROSPERO (CRD42020208275) on 1 October 2020.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".