What does it take to learn from patient and caregiver experiences to improve healthcare? Key considerations from patients, caregivers, and healthcare professionals at a Canadian hospital
Bibliographic record
Abstract
Healthcare systems face ongoing challenges in advancing patient experience – an integral component of care quality. Patients and caregivers can play a critical role in identifying ways to improve experience. However, organisations tend to only learn from their experiences using a narrow set of approaches – typically patient experience surveys and complaints data – instead of drawing from a variety of methods such as focus groups or staff rounding. While surveys can yield a large sample and general trends, they tend to only provide a snapshot of a person’s healthcare experiences, are insufficient in fully capturing feedback, and are less likely to be completed by people from equity-deserving groups. As a result, survey data alone do not typically reflect the experiences of communities served by healthcare organisations. In this paper, we share findings from a qualitative exploratory study where we asked twenty-three patient and caregiver partners and healthcare professionals (e.g., leaders and staff) at a Canadian hospital about strategies to learn from experiential data to improve healthcare. We interviewed participants from a large, urban, academically affiliated community hospital in southern Ontario, which serves one of the most diverse communities in Canada. Using thematic analysis, we identified five important conditions for optimal collection of patient experience data: the need for organisations to communicate a clear purpose, create psychological safety, continuously learn throughout the patient journey, collect community level data, and use multiple approaches to learn about experience. Adopting these conditions has the potential to widen the breadth of experience data collected by organisations, ensuring that no voices are excluded from shaping quality improvement initiatives.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.022 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".