Patient Perspectives on Technological Barriers and Implementation Strategies Leveraged During a Real-World Remote Symptom Monitoring Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 it