Barriers and facilitators of digital health intervention uptake among healthcare providers in cardiovascular disease prevention: qualitative results from a systematic review
Bibliographic record
Abstract
Abstract Background Evidence-based digital health interventions (DHIs) can aide cardiovascular disease (CVD) prevention (e.g., though telemonitoring, mHealth apps, etc). Despite this, the global uptake of DHIs in CVD prevention remains slow [1]. Healthcare providers (HCPs) often fulfil the role of gatekeepers and implementers of DHIs for patient care and their perspectives are valuable to understand barriers and facilitators of DHI uptake. Purpose The purpose of this study was to synthesise barriers and facilitators to DHI uptake in CVD (primary and secondary) prevention reported in the international scientific literature from the perspectives of HCPs. Methods We conducted a systematic review of qualitative and quantitative primary studies published January 2020 to May 2024 which explored HCPs’ perspectives of DHIs in CVD prevention. Excluded were non-peer reviewed articles, review papers, studies with non-generalisable feedback on a specific product, and studies with non-patient-facing digital tools. We retrieved records from Ovid MEDLINE, EMBASE, CINAHL, ACM Digital Library, Web of Science, Google scholar, IEEE Xplore, and Scopus. We used the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields to assess the quality of included studies [2]. For this abstract, we present partial findings of the systematic review pertaining to the qualitative data extracted from included studies. We extracted and coded reported barriers and facilitators according to an inductively created codebook and categorized each to one of four roadblocks described by the World Heart Federation roadmap on digital health in cardiology: ‘health system’, ‘health workforce’, ‘patient’ and ‘technological’ [1]. We used vote counting to describe which barriers and facilitators are most prevalent across studies. Results A total of 7,638 search results from the databases was retrieved. Following de-duplication and abstract/full-text screening, 110 studies reporting qualitative findings were included. Our findings represent a total of 2,594 HCP perspectives with their geographic distribution shown in figure 1. Study quality was median 80% (range 40-100%) out of a possible 100%. Across all categories ‘health system’, ‘health workforce’, ‘patient’ and ‘technological’, we extracted 82 barriers and 84 facilitators from the perspectives of HCPs. Table 1 shows all barriers and facilitators reported in ≥11 (10%) of included studies. Conclusion Our findings provide a global perspective on the current factors encouraging and hindering HCPs from adopting DHIs into their practice. Implementation scientists can use these findings to plan best approaches for efficient and long-term uptake of DHIs in CVD prevention. Overall, our findings align with the World Health Organization (WHO) Global Strategy on Digital Health 2020-2025 strategic objective 4: advocating for people-centred health systems that are enabled by digital health.Geographic distribution of respondents Barriers and facilitators
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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 | 0.031 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".