Patient Preferences for Using Remote Care Technology in Heart Failure: Discrete Choice Experiment
Bibliographic record
Abstract
Background: Remote care technology has been used to bridge the gap between health care in a clinical setting and in the community, all the more essential post-COVID. Patients with chronic conditions may benefit from interventions that could provide more continuous and frequent monitoring of their disease process and support self-management. A common barrier, however, is the lack of engagement with technological interventions or devices that provide care remotely, which could lead to loss of resources invested and reduced quality of care. Objective: This discrete choice experiment elicits the preferences of patients with heart failure with regard to potential remote care technologies that they would be willing to engage with and, in turn, creates a hierarchy of factors that can affect engagement for use within future technology design. Methods: A survey was created using discrete choice design and with input from a patient and public involvement group. It was distributed online via social media to patients with heart failure and to patient support groups. The attributes used for the experiment were based on a previous systematic review looking at factors that affect engagement in remote care and which generated five central themes, each of which was assigned to an attribute directly: communication (increasing interaction between patients and health care staff/carers/other patients), clinical care (improving the quality of care compared to established practice), education (providing tailored information to help with self-care and reduce uncertainty), ease of use (the technical aspects of the intervention are easy to handle without issues), and convenience (the intervention fits well around the patient's lifestyle and requires minimal effort). Each of the five themes had two levels, positive and negative. The survey presented participants with multiple forced-choice two-alternative scenarios of remote care, which allowed them to trade attributes according to their preference. The results were analyzed using binary logit to obtain preference weights for each attribute. Results: A total of 93 completed responses were entered into the analysis. The results of the binary logit created coefficients for each attribute, which equated to the relative preference of the associated themes: clinical care, 2.022; education, 1.252; convenience, 1.245; ease of use, 1.155; communication, 1.040. All calculated coefficients were statistically significant (P<.01). Conclusions: The results show that, in this cohort of patients with heart failure, the most preferred factor, clinical care, has enough value to be traded for approximately any two other factors. It also shows that the factor of communication is the least preferred attribute. Technology designers can use the associated preference weights to determine the relative increase of value perceived by patients by adding in certain attributes, with the greatest gains achieved by prioritizing clinical care. This would result in increased engagement in a chronic heart failure population that would benefit most from remote care.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".