Family Caregivers’ Perspectives on the Potential of Drone-Based Medication Delivery in Palliative Home Care: Qualitative Focus Group Study
Bibliographic record
Abstract
Background: Palliative care supports individuals with incurable, life-threatening illnesses, focusing on symptom management and quality of life. Access to timely care, including essential medications, is often limited, particularly in rural areas, leading to gaps in home-based care. Digital health technologies, including drone-based delivery systems, have the potential to address such logistical challenges. For these technologies to be effective, they must be adapted to the specific needs of patients and caregivers, which often differ from general health care contexts, especially in remote areas. Objective: This study aimed to identify user needs and requirements for a drone-based medication delivery system designed to supplement traditional courier services in palliative care. The study also sought to explore practical considerations for integrating drones into home-based care workflows, with the goal of informing both technical design and clinical applicability. Methods: We conducted 1 focus group (FG) and 4 semistructured individual interviews with family caregivers involved in home-based palliative care. Participants were recruited via local palliative care services in rural regions. The discussions focused on experiences with palliative care, medication logistics, needs, expectations, and concerns regarding drone-assisted delivery. Interviews and the FG were audio-recorded, transcribed, and analyzed using structured qualitative content analysis. Results: A total of 10 caregivers participated (mean age 68.6, SD 12.3 years) in this study. Six participants took part in the FG and 4 participants were part of individually conducted interviews. Caregivers frequently reported long travel distances to obtain medications, sometimes leaving patients unattended and noting medication shortages, especially in the afternoons and on weekends. Participants highlighted the potential of drones to supplement existing courier services during periods of high demand. At the same time, caregivers expressed concerns about limited technical skills, particularly regarding mobile apps for ordering deliveries, and emphasized the need for simple, user-friendly systems. . Conclusions: These findings reveal logistical gaps in palliative care medication supply and design requirements for drone-based delivery systems. There is a clear demand for faster, more reliable access to essential medication, especially during evenings and weekends. Whether drone-assisted delivery can reduce hospitalizations and support patients' wishes to remain at home warrants further research. Specialized outpatient palliative care teams are central to eHealth adoption, providing technical guidance, emotional support, and confidence in using new technologies. Their involvement in communication and training is critical. Strengthening digital health literacy requires individual training and structural adjustments to meet the needs of older caregivers and those with limited digital affinity. Concerns about losing personal interaction remain significant; digital tools should complement, not replace, face-to-face care. Timely medication access could reduce caregiver stress and improve quality of life by supporting symptom control at home. To foster acceptance, caregivers need hands-on experience. Future studies should test drone delivery in real-world settings, addressing technological, psychological, and social factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".