Patient‐reported experience measures are essential to improving quality of care for chronic wounds: An international qualitative study
Bibliographic record
Abstract
Traditional quality measures for chronic wounds have focused on objective outcomes that are challenging to risk adjust, lack patient input, and have limited ability to inform quality improvement interventions. Patient-reported experience measures (PREMs) provide information from the patient perspective regarding health care quality and have potential to improve patient-centredness, increase care efficiency, and generate actionable data for quality improvement. The purpose of this study was to understand patient experiences and health care processes that impact quality of care among patients with chronic wounds. Sixty patients at least 18 years of age with various wound aetiologies were recruited from Canada, Denmark, The Netherlands, and the United States as part of a larger phase 1 qualitative study to develop a patient-reported outcome measure for chronic wounds (WOUND-Q). All patients had a chronic wound for at least 3 months, were fluent in their native speaking language, and able to participate in a one-on-one semi-structured interview. Interviews were digitally recorded and transcribed verbatim. Interpretive description was used to identify recurrent themes relating to patient experience and quality of care. We identified five domains (care coordination, establishing/obtaining care, information delivery, patient-provider interaction, and treatment delivery) and 21 sub-domains (access to patient information, interdisciplinary communication, encounter efficiency, provider availability, specialist referral, staff professionalism, travel/convenience, modality, reciprocity, understandability/consistency, accountability, continuity, credentials, rapport, appropriateness, complication management, continuity, environment/setting, equipment and supply needs, expectation, and patient-centred) as potential opportunities to measure and improve quality of care in the chronic wound population. PREMs for chronic wounds represent an important opportunity to engage patients and longitudinally assess quality across clinical settings and providers. Future research should focus on developing PREMs to complement traditional objective and patient-reported outcome measures for chronic wounds.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".