Qualitative Study of Health Care Team Perception of the Benefits and Limitations of Remote Symptom Monitoring
Bibliographic record
Abstract
PURPOSE Remote symptom monitoring (RSM) using electronic patient-reported outcomes (ePROS) connects patients and health care teams between appointments. Patient-perceived benefits and drawbacks of RSM are well-known, but health care team members' perceptions are less clear. METHODS Health care team members from the University of Alabama at Birmingham and the University of South Alabama Health Mitchell Cancer Institute participated in semi-structured qualitative interviews to explore their experiences and perspectives on RSM benefits and limitations. Interviews were audio-recorded, transcribed, and analyzed inductively using NVivo software to identify recurring themes and exemplary quotes. RESULTS Thirty oncology health care team members, including physicians (n = 9), nurse practitioners (n = 2), nurses (n = 8), nonclinical navigators (n = 7), and administrators (n = 4), were interviewed. Findings were organized into five major themes: three benefits ( Proactive, Improved Patient-Health Care Team Relationship , and Patient Engagement and Symptom Reporting ) and two limitations ( Health Care Team-Perceived Limited Patient Buy-In or Awareness and Workload and Workflow Issues ). Health care team members perceived that RSM improved their ability to support patients and the quality of care delivered to patients by promoting proactive management, strengthening the patient-health care team relationship, and engaging patients in symptom reporting. Despite positive perceptions, health care team members also voiced drawbacks of RSM related to the lack of patient buy-in or awareness and increased workload and disrupted workflow. CONCLUSION Although health care team members recognized the benefits of RSM as a standard of care, future work is necessary to address identified limitations to support wide-scale implementation of RSM in oncology practices.
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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 | 0.001 | 0.001 |
| 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".