Barriers to and facilitators of the uptake of digital health technology in cardiology: a systematic review
Bibliographic record
Abstract
Abstract Background Digital health technology has the potential to revolutionize the quality and efficiency of healthcare delivery. However, the uptake of digital health technology has been low in clinical practice. The factors that contribute to the limited adoption of digital health technology, particularly in cardiology, are unclear. Purpose We identified and synthesized the barriers to and facilitators of digital health technology uptake in cardiology, with a focus on provider- and patient- level barriers and facilitators. Methods We searched MEDLINE, EMBASE and CINAHL databases for studies published January 2000 - December 2019 that reported barriers to and/or facilitators of digital health technology adoption in cardiology. Two reviewers screened and extracted data independently. We conducted a thematic analysis to identify major themes pertaining to digital health technology uptake by both providers and patients. Results The search identified 3062 unique studies, of which 23 qualitative studies met eligibility criteria. Seventeen studies included semi-structured interviews and 6 included focus groups. Five (22%) studies reported provider-level facilitators, which included technology usability, integration into clinical workflow, and improved patient outcomes. Eighteen (78%) studies reported patient-level facilitators, which included ongoing technical support, improved access to healthcare services, and improved self-management. Six (26%) studies reported provider-level barriers, which included lack of integration into clinical workflow, increased healthcare costs, and lack of validation and reliability of technology. Finally, 19 (83%) studies reported patient-level barriers which included lack of knowledge about technology, limited internet access, and physical impairments making use of technology difficult. Conclusions Identifying barriers to and facilitators of digital health technology could help improve its uptake in cardiology. The findings of this study can be used to inform researchers, clinicians, and stakeholders who wish to develop and implement digital health technologies that meet the needs of providers and patients. Funding Acknowledgement Type of funding source: None
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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 | 0.033 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".