Understanding Patient Reluctance to the Remote Monitoring of Cardiac Implantable Electronic Devices
Bibliographic record
Abstract
Objective: To understand patient’s reasoning for declining remote monitoring of their cardiac device. Is a patient more likely to proceed with remote monitoring if they are aware of the benefits and limitations of remote monitoring?Background: It is now possible to assess pacemakers and defibrillators remotely through the use of personal monitors. Remote monitoring has many evidence based benefits for the patient and device clinic, and is now an integral part of the standard of medical care for CIED patients in Canada. Despite this, a minority of patients decline remote monitoring. We have a poor understanding as to why this might be, with little research in contemporary literature looking at this area.Methods: A descriptive survey, questionnaire study was used having both quantitative and qualitative features. This allowed for the primary reasons for patients declining remote monitoring to be concisely identified, using thematic analysis. It assessed a patient’s knowledge of the benefits of remote monitoring, looking for a relationship between this and likelihood to proceed with remote monitoring.Results: Loss of human contact appears to be a predominant concern, confirming patient’s reported experiences in Ottenberg’s study and as suggested in the 2015 HRS statement. Privacy and security fears were also highlighted. Surprisingly fear of technology, in a mainly elderly population, was rarely mentioned. Half of the participants in this study stated that they would be likely to proceed with remote monitoring after having read the evidence based benefits for its use.Conclusions and Recommendations: Patient acceptance of remote monitoring can be improved by educating them to its benefits and limitations. To alleviate privacy concerns, device companies should look at the feasibility of having their remote monitoring servers physically based in Canada.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".