User-Centered Adaptation of an Existing Heart Failure Telemonitoring Program to Ensure Sustainability and Scalability: Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Telemonitoring interventions for the management of heart failure have seen limited adoption in Canadian health systems, but isolated examples of telemonitoring programs do exist. An example of such a program was launched in a specialty heart failure clinic in Toronto, Canada, and a recent implementation evaluation concluded that reducing the cost of delivering the program is necessary to ensure its sustainability and scalability. OBJECTIVE The objectives of this study were to (1) understand which components of the telemonitoring program could be modified to reduce costs and adapted to other contexts while maintaining program fidelity and (2) describe the changes made to the telemonitoring program to enable its sustainability within the initial implementation site and scalability to other health organizations. METHODS Semistructured interviews probed the experiences of patients (n=23) and clinicians (n=8) involved in the telemonitoring program to identify opportunities for cost reduction and resource optimization. Ideas for adapting the program were informed by the interview results and prioritized based on (1) potential impact for sustainability and scalability, (2) feasibility, and (3) perceived risks to negatively impacting the program’s ability to yield desired health outcomes. RESULTS A total of 5 themes representing opportunities for cost reduction were discussed, including (1) Bring Your Own Device (BYOD), (2) technical support, (3) clinician role, (4) duration of enrollment, and (5) intensity of monitoring. The hardware used for the telemonitoring system and the modalities of providing technical support were found to be highly adaptable, which supported the decision to implement a BYOD model, whereby patients used their own smartphone, weight scale, and blood pressure cuff. Changes also included the development of a website aimed at reducing the burden on a technical support telehealth analyst. In addition, the interviews suggested that although it is important to have a clinician who is part of a patient’s circle of care monitoring telemonitoring alerts, the skill level and experience were moderately adaptable. Thus, a registered nurse was determined to be more cost-effective and was hired to replace the existing nurse practitioners in the frontline management of telemonitoring alerts and take over the technical support role from a telehealth analyst. CONCLUSIONS This study provides a user-centered example of how necessary cost-reduction actions can be taken to ensure the sustainability and scalability of telemonitoring programs. In addition, the findings offer insights into what components of a telemonitoring program can be safely adapted to ensure its integration in various clinical settings.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".