Patient and Provider Perspectives on Symptom Monitoring During Outpatient Chemotherapy: Interview Study
Bibliographic record
Abstract
BACKGROUND: Fluctuating symptoms and side effects are common during outpatient cancer treatment, and approaches to monitoring symptoms vary widely across providers, patients, and clinical settings. To design a remote symptom monitoring system that patients and providers find to be useful, it may be helpful to understand current clinical approaches to monitoring and managing chemotherapy-related symptoms among patients and providers and assess how more frequent and systematic assessment and sharing of data could improve patient and provider experiences. OBJECTIVE: The goals of this study were to learn about patient and provider perspectives on monitoring symptoms during chemotherapy, understand barriers and challenges to effective symptom monitoring at one institution, and explore the potential value of remote symptom monitoring between provider visits. METHODS: A total of 15 patients who were currently undergoing or had recently completed chemotherapy and 7 oncology providers participated in semistructured interviews. Interviews were transcribed and coded using an iterative thematic analysis approach. The study was conducted at a National Cancer Institute-Designated Comprehensive Cancer Center. RESULTS: Four main themes were discussed by patients and providers: (1) asynchronous nature of current methods for tracking and managing symptoms, (2) variability in reported symptoms due to patient factors, (3) limitations of existing communication channels, and (4) potential value of real-time remote symptom monitoring during chemotherapy. Current asynchronous methods and existing communication channels resulted in a disconnect between when symptoms are most severe and when conversations about symptoms happen, a situation further complicated by memory impairments during chemotherapy. Patients and providers both highlighted improvements in patient-provider communication as a potential benefit of remote real-time symptom monitoring. Providers also emphasized the value of temporal data regarding when symptoms first emerge and how they progress over time, as well as the potential value of concurrent activity or other data about daily activities and functioning. Patients noted that symptom monitoring could result in better preparation for subsequent treatment cycles. CONCLUSIONS: Both patients and providers highlighted significant challenges of asynchronous, patient-initiated, phone-dependent symptom monitoring and management. Oncology patients and providers reported that more routine remote monitoring of symptoms between visits could improve patient-provider communication, prepare patients for subsequent chemotherapy cycles, and facilitate provider insight and clinical decision-making with regard to symptom management.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".