Development and implementation of a patient reported experience measurement program to transform cancer care.
Bibliographic record
Abstract
343 Background: Improving the patient experience is central to providing high-quality, value-based care. Patient reported experience measures (PREMs) are tools to evaluate and improve quality of care. Your Voice Matters (YVM) is a validated survey created by Cancer Care Ontario that captures patient experience in outpatient settings at Regional Cancer Centres across Ontario, Canada. While most hospitals actively collect patient experience data, few have moved to action feedback to improve outcomes. Princess Margaret Cancer Centre (PM) implemented a systematic approach to PREMs evaluation to advance operational excellence in an outpatient setting. Methods: PM distributes the YVM survey monthly to patients with outpatient visits via email using a Redcap survey platform (93% of patient emails are on file). In addition to the standardized YVM questions, the survey captures respondent comments in an open text field asking about one thing PM can improve and comments are thematically coded and trended monthly. Patients reporting a very poor experience with care (defined as an overall experience of 1/5) who wish to connect with PM are contacted to provide additional information. Key performance indicators (KPIs) were established and visualized in run charts in a Power BI dashboard. KPIs were disseminated to staff and leadership to identify opportunities for improvement. Results: Starting in 2022, approximately 10,600 surveys were disseminated monthly to patients and 1,100 surveys returned (10% response rate) with 10-15 patients contacted each month regarding their poor experience. Long wait times was the top concern across three main outpatient clinics; in the Gynecology clinics, in March 2022, only 45% (35/77) of respondents rated wait times as very good (5/5) and 32% (11/34) of comments were wait times complaints. A comprehensive root cause analysis found that the visit booking system, distribution of clinics across the weekdays, and supports in clinic were key contributors to long wait times. In response, Gynecology clinic resources were reorganized to launch a new Physician Assistant clinic on the underutilized Friday afternoon clinic timeslot, a new streamlined visit process was established, and the booking system was optimized. Within six months, there was a 39% improvement in wait times on weekdays, a 60% increase in the number of patients waiting less than 30 minutes, and a 37% increase in visit capacity on underutilized Fridays. The corresponding patient experience data over the same timeframe improved seen as an increase from 45% to 52% of patients who rated wait times to see the provider as 5/5. Conclusions: A programmatic approach to PREMs evaluation and response can drive improvement in practice. Facilitators include a data-driven methodologic approach to quality improvement, leadership engagement at all levels, and elevation of frontline staff as local experts and change agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".