Barriers and Enablers to Routine Clinical Implementation of Cardiac Implantable Electronic Device Remote Monitoring in Australia Among Cardiologists, Cardiac Physiologists, Nurses, and Patients: Interview Study
Bibliographic record
Abstract
Background: Remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) has demonstrated many patient and health care system benefits. Consequently, the use of RM technology for patients with CIEDs is the standard of care as highlighted by international guidelines. However, RM has not yet been integrated into universal, routine clinical practice. Objective: We aimed to establish key stakeholder perspectives on the barriers and enablers of CIED RM implementation and to apply the theoretical domain framework to highlight the most effective approaches to facilitate routine adoption of CIED RM. Methods: This was a qualitative study, using semistructured interviews to explore the barriers and enablers encountered when incorporating RM into CIED management. Participants included cardiologists, cardiac clinicians or physiologists, nurses, and patients. Interviews were transcribed verbatim and analyzed through inductive thematic analysis and deductive approaches using the NVivo (version 14; QRS International Pty Ltd) software. The theoretical domains framework was used to understand barriers and enablers. In the inductive phase, we did not assess trustworthiness, as our thematic analysis approach views data as interpretations rather than objective truths. In the deductive phase, we conferred to ensure consistency in theme alignment with existing frameworks. Results: Interviews were conducted among 35 participants (16 patients, 10 cardiologists, and 9 cardiac physiologists and nurses). We identified 5 main themes and their associated subthemes, with 1 representing an enabler and 4 representing barriers. They were: (1) patient benefits from RM, such as improved CIED and cardiovascular management, and improved patient-centered care; (2) insufficient allocation of CIED RM resources, which included insufficient RM clinic funding and staffing, insufficient RM service reimbursement, and RM infrastructure and access inequity; (3) suboptimal management of data, which includes inconsistent RM alert interpretation and management, lack of guidance for clinic staff on RM data management, and an increased alert burden for clinics; (4) insufficient patient education post-CIED implant, this was attributed to limited health care worker availability and resulted in inadequate patient CIED and RM knowledge postimplant and patient anxiety associated with RM; and (5) patient engagement with CIED management, which included the need for increased patient interaction with RM alerts and the ability to share data with patients. These subthemes were mapped to 6 specific domains of the theoretical domains framework: "Beliefs About Capabilities," "Environmental Context and Resources," "Beliefs About Consequences," "Knowledge," "Emotions," and "Goals." Conclusions: Patient engagement was identified in 3 of the 5 themes describing barriers and enablers to RM. These highlight the importance of addressing patient engagement with RM to better implement and integrate the use of RM into routine clinical practice. Barriers and enablers extend across multiple domains and suggest that a multipronged approach is required to translate the gold standard care of RM to routine clinical practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".