Barriers and reliability of patient experience evaluation in Ontario: perspectives of healthcare providers, managers, and policymakers
Bibliographic record
Abstract
Purpose Patient experience (PE) evaluation can identify critical issues in healthcare quality. Various methods are used for PE evaluation in the healthcare system in Ontario; however, evidence suggests that PE evaluation is not systematically performed and has not received substantial buy-in from healthcare providers. This study explores the perspectives of healthcare providers, managers and policymakers in Ontario on PE evaluation methods, barriers, utility and reliability. Design/methodology/approach The study used a qualitative descriptive design. Twenty-one semistructured interviews were conducted with healthcare providers, managers and policymakers in Ontario between April 2018 and May 2019. The authors used thematic analysis to analyze the data. The Consolidated Criteria for Reporting Qualitative Research quality criteria were used. Findings Barriers to PE evaluation include evaluation cost and the time and effort required to collect and analyze the data. Several factors affect the reliability of the evaluation, resulting in an unrealistically high level of patient satisfaction. These include the inclusivity of evaluation, the subjective nature of patient feedback, patients' concerns about health service continuity and the anonymity of evaluation. Participants were skeptical about the meaningfulness of evaluation because it may only yield general information that cannot be acted upon by healthcare providers, managers and policymakers for quality improvement. Originality/value This paper reveals that many healthcare providers, managers and policymakers do not see a tangible value in PE evaluation, regardless of Ontario's patient-centeredness and “patient first” rhetoric. An improvement in evaluation methods and a cultural change in the healthcare system regarding the value of PE are required to foster a better appreciation of the benefits of PE evaluation.
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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 | 0.061 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".