Exploring the Impact of a Remote Monitoring System for Palliative and End-of-Life Care (CARE-PAC): Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: In the United Kingdom, access to and the quality of palliative and end-of-life care (PEOLC) vary widely. In the final months of life, many patients face avoidable accident and emergency (A&E) visits and hospital admissions, driven by gaps in out-of-hours support and poorly coordinated care. This not only increases stress for patients and carers but also places avoidable strain and cost on the National Health Service (NHS). There is an urgent need for more compassionate, person-centered models that support people to remain at home, improve their quality of life (QoL), and reduce unnecessary use of acute services. OBJECTIVE: This study aimed to explore the usability, user experiences, and impact of the digital dyadic remote monitoring Care and Support System for Patients and Carers (CARE-PAC) for patients in the last year of life, their informal carers, and health professionals involved in their care. METHODS: Patients and informal carers were recruited to use CARE-PAC for up to 12 weeks. A mixed methods approach was used. Quantitative methods included the use of validated QoL scales and the System Usability Scale (SUS). Paired QoL and usability data were analyzed using the Wilcoxon (Pratt) signed-rank test, while unpaired usability data were analyzed using the Wilcoxon rank-sum test. Qualitative methods involved short catch-up calls, in-depth interviews, and focus groups conducted using topic guides informed by the domains of the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. Data were analyzed thematically. RESULTS: CARE-PAC was implemented across 5 UK clinical sites with 26 participants (13 patient-carer dyads). No significant changes were observed in patients' total QoL scores; however, significant improvements were seen in the "overall QoL" and "social" domains, alongside a significant decline in the "physical" domain. Carers showed no significant changes across total or domain-specific QoL scores. Usability was rated highly by patients (mean 87.9, SD 12.4) and carers (mean 94.7, SD 3.8), indicating an excellent user experience. Health care professionals (HCPs) reported lower usability scores (mean 63.6, SD 15.6), falling below average but above the threshold for poor usability. Thematic analysis of qualitative data gathered via catch-up calls (all patient-carer dyads), in-depth interviews (2 patients-2 carers), and 4 focus groups/1 interview (12 HCPs) identified 4 key themes: impact on care experiences, reflections and satisfaction, implementation challenges, and future directions. CONCLUSIONS: CARE-PAC is a usable, feasible, and acceptable remote monitoring and support system for patients in the last year of life, their carers, and HCPs. It enables real-time identification of needs and has shown positive impacts on the QoL of both patients and carers. These findings support the need for further research to evaluate its effectiveness at scale and explore pathways for wider implementation in PEOLC.
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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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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 | 0.001 |
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".