“You need a team”: perspectives on interdisciplinary symptom management using patient-reported outcome measures in hemodialysis care—a qualitative study
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) are standardized instruments used for assessing patients' perspectives on their health status at a point in time, including their health-related quality of life, symptoms, functionality, and physical, mental, and social wellbeing. For people with kidney failure receiving hemodialysis, addressing high symptom burden and complexity relies on care team members integrating their expertise to achieve common management goals. In the context of a program-wide initiative integrating PROMs into routine hemodialysis care, we aimed to explore patients' and clinicians' perspectives on the role of PROMs in supporting interdisciplinary symptom management. METHODS: We employed a qualitative descriptive approach using semi-structured interviews and observations. Eligible participants included adult patients receiving intermittent, outpatient hemodialysis for > 3 months, their informal caregivers, and hemodialysis clinicians (i.e., nurses, nephrologists, and allied health professionals) in Southern Alberta, Canada. Guided by thematic analysis, team members coded transcripts in duplicate and developed themes iteratively through review, refinement, and discussion. RESULTS: Thirty-three clinicians (22 nurses, 6 nephrologists, 5 allied health professionals), 20 patients, and one caregiver participated in this study. Clinicians described using PROMs to coordinate care across provider types using the resources available in their units, whereas patients tended to focus on the perceived impact of this concerted care on symptom trajectory and care experience. We identified 3 overarching themes with subthemes related to the role of PROMs in interdisciplinary symptom management in this setting: (1) Integrating care for interrelated symptoms ("You need a team", conducive setting, role clarity and collaboration); (2) Streamlining information sharing and access (symptom data repository, common language for coordinated care); (3) Reshaping expectations (expectations for follow-up, managing symptom persistence). CONCLUSIONS: We found that use of PROMs in routine hemodialysis care highlighted symptom interrelatedness and complexity and helped to streamline involvement of the interdisciplinary care team. Issues such as role flexibility and resource constraints may influence sustainability of routine PROM use in the outpatient hemodialysis setting.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".