How the Routine Use of Patient-Reported Outcome Measures for Hemodialysis Care Influences Patient-Clinician Communication
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patient-reported outcome measures invite patients to self-report aspects of their quality of life and have been reported to enhance communication with clinicians. We aimed to examine how routine use of patient-reported outcome measures in in-center hemodialysis units influenced patient-clinician communication. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A concurrent, longitudinal, mixed-methods approach was used. We used data from a cluster randomized controlled trial of 17 hemodialysis units in northern Alberta that introduced a patient-reported outcome measures intervention. Patient-clinician communication was assessed using a modified Communication Assessment Tool. Using interpretive description, we explored patients' and nurses' perceptions of communication pertaining to routine patient-reported outcome measure use. Through purposeful sampling, we interviewed ten patients and eight nurses and conducted six observations in the dialysis units, which were documented in field notes. We reviewed 779 patient responses to open-ended survey questions from randomized controlled trial data. Qualitative data were thematically analyzed. RESULTS: Overall, patient-reported outcome measure use did not substantively improve patient-clinician communication. There was a small positive change in mean total Communication Assessment Tool scores (range, 1-5) from baseline to 12 months in patient-reported outcome measure use units (0.25) but little difference from control group units that did not use patient-reported outcome measures (0.21). The qualitative findings provide in-depth insights into why patient-reported outcome measure use did not improve patient-clinician communication. The purpose of patient-reported outcome measure use was not always understood by patients and clinicians; patient-reported outcome measures were not implemented as originally intended in the trial, despite clinician training; there were challenges using patient-reported outcome measures as a means to communicate; and patient-reported outcome measure use was perceived to have limited value. CONCLUSIONS: While patient-reported outcome measures use did not improve patient-clinician communication, qualitative data suggest implementation challenges, including limited clarity of purpose and perceived limited value, that may have limited the effectiveness of the intervention.
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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 | 0.086 | 0.210 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".