Patient Perspectives on Technological Barriers and Implementation Strategies Leveraged During a Real-World Remote Symptom Monitoring Program
Bibliographic record
Abstract
PURPOSE: Remote symptom monitoring (RSM) using electronic patient-reported outcomes leverages digital technologies to gather real-time information on patient experiences for symptom management. This study reports a formative evaluation of technology-related barriers encountered by patients participating in RSM and implementation strategies used to address those barriers in real-world, large-scale RSM implementations. METHODS: Purposive sampling was conducted to recruit patients diagnosed with cancer and participating in RSM at the University of Alabama at Birmingham and USA Health Mitchell Cancer Institute for semi-structured interviews. Interviews were coded to identify technology-related barriers using a constant comparative method. Expert Recommendations for Implementing Change list was used to address the barriers to optimize RSM implementation. Barrier-associated themes from the interviews were mapped to implementation strategies. RESULTS: . Themes were mapped to the implementation strategies as identified by the implementation team. Eight total implementation strategies were used to address these technology barriers: (1) assess for readiness and identify barriers and facilitators, (2) obtain and use patients/consumers and family/caregiver feedback, (3) involve patients/consumers and family members/caregivers, (4) access new funding, (5) change physical structure and equipment, (6) centralize technical assistance, (7) prepare patients/consumers to be active participants, and (8) intervene with patients/consumers to enhance uptake and adherence. CONCLUSION: Technology-related barriers may limit the uptake of RSM by patients. Addressing these barriers through multimodel assessment and intervention strategies is crucial to ensuring successful implementation of RSM in real-world settings.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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 | 0.000 |
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".