A Personalized and Interactive Web-Based Health Care Innovation to Advance the Quality of Life and Care of Patients With Heart Failure (ACQUIRE-HF): A Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a progressive, debilitating, and complex disease, and due to an increasing incidence and prevalence, it represents a global health and economic problem. Hence, there is an urgent need to evaluate alternative care modalities to current practice to safeguard a high level of care for this growing population. OBJECTIVE: Our goal was to examine the feasibility of engaging patients to use patient-centered and personalized tools coupled with a Web-based, shared care and interactive platform in order to empower and enable them to live a better life with their disease. METHODS: We used a mixed methods, single-center, pre-post design. Patients with HF and reduced left ventricular ejection fraction (n=26) were recruited from the outpatient HF clinic at Odense University Hospital (Svendborg Hospital), Denmark, between October 2015 and March 2016. Patients were asked to monitor their health status via the platform using the standardized, disease-specific measure, the Kansas City Cardiomyopathy Questionnaire (KCCQ), and to register their weight. A subset of patients and nursing staff were interviewed after 3-month follow-up about their experiences with the platform. RESULTS: Overall, patients experienced improvement in patient-reported health status but deterioration in self-care behavior between baseline and 3-month follow-up. The mean score reflecting patient expectations toward use prior to start of the study was lower (16 [SD 5]) than their actual experiences with use of the platform (21 [SD 5]) after 3-month follow-up. Of all patients, 19 completed both a baseline and follow-up KCCQ. A total of 9 experienced deterioration in their health status (range from 3-34 points), while 10 experienced an improvement (range from 1-23 points). The qualitative data indicated that the majority of patients found the registration and monitoring on the platform useful. Both nursing staff and patients indicated that such monitoring could be a useful tool to engage and empower patients, in particular when patients are just diagnosed with HF. CONCLUSIONS: The use of patient tracking and monitoring of health status in HF using a standardized and validated measure seems feasible and may lead to insights that will help educate, empower, and engage patients more in their own disease management, although it is not suitable for all patients. Nursing staff found the patient-centered tool beneficial as a communication tool with patients but were more reticent with respect to using it as a replacement for the personal contact in the outpatient clinic.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".