Patient-Perceived Benefits and Limitations of Standard of Care Remote Symptom Monitoring During Cancer Treatment
Bibliographic record
Abstract
Introduction Remote symptom monitoring (RSM) allows patients to electronically self-report symptoms to their healthcare team for individual management. Clinical trials have demonstrated overarching benefits; however, little is known regarding patient-perceived benefits and limitations of RSM programs used during patient care. Methods This prospective qualitative study from December 2021 to May 2023 included patients with cancer participating in standard-of-care RSM at the University of Alabama at Birmingham (UAB) in Birmingham, AL, and the Univeristy of South Alabama (USA) Health Mitchell Cancer Institute (MCI) in Mobile, AL. Semi-structured interviews focused on patient experiences with and perceptions surrounding RSM participation. Interviews occurred over the phone, via digital videoconference, or in person, at the convenience of the patient. Grounded theory was used to conduct content coding and identify recurring themes and exemplary quotes using NVivo. Results Forty patients (20 UAB, 20 MCI) were interviewed. Participants were predominately female (87.5%), aged 41-65 (50%), and married (57.5%). Data is consistent with local demographics, comprising mainly White (72.5%) and 27.5% Black individuals. Three main themes emerged regarding perceived benefits of RSM: (1) Facilitation of Proactive Management , identifying the patient’s needs and intervening earlier to alleviate symptom burden; (2) Promotion of Symptom Self-Management , providing patients autonomy in their cancer care; and (3) Improvement in Patient-Healthcare Provider Relationships , fostering genuine connections based on healthcare team’s responses. However, participants also noted Perceived Limitations of RSM ; particularly when support of symptom management was unnecessary, ineffective, or felt impersonal. Conclusion This study focused on patient experiences when utilizing a RSM program while undergoing treatment for cancer and found benefits to its implementation that extended beyond symptom management. At the same time, patients noted drawbacks experienced during RSM, which can help with future tailoring of RSM programs. Patient perceptions should be regularly assessed and highlighted for successful and sustained implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".