Cost-Utility of the eHealth Application ‘Oncokompas’, Supporting Incurably Ill Cancer Patients to Self-Manage Their Cancer-Related Symptoms: Results of a Randomized Controlled Trial
Bibliographic record
Abstract
Evidence on the cost-effectiveness of eHealth in palliative care is scarce. Oncokompas, a fully automated behavioral intervention technology, aims to support self-management in cancer patients. This study aimed to assess the cost-utility of the eHealth application Oncokompas among incurably ill cancer patients, compared to care as usual. In this randomized controlled trial, patients were randomized into the intervention group (access to Oncokompas) or the waiting-list control group (access after three months). Healthcare costs, productivity losses, and health status were measured at baseline and three months. Intervention costs were also taken into account. Non-parametric bootstrapping with 5000 replications was used to obtain 95% confidence intervals around the incremental costs and quality-adjusted life years (QALYs). A probabilistic approach was used because of the skewness of cost data. Altogether, 138 patients completed the baseline questionnaire and were randomly assigned to the intervention group (69) or the control group (69). In the base case analysis, mean total costs and mean total effects were non-significantly lower in the intervention group (-€806 and -0.01 QALYs). The probability that the intervention was more effective and less costly was 4%, whereas the probability of being less effective and less costly was 74%. Among patients with incurable cancer, Oncokompas does not impact incremental costs and seems slightly less effective in terms of QALYs, compared to care as usual. Future research on the costs of eHealth in palliative cancer care is warranted to assess the generalizability of the findings of this study.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".