The routine collection of patient-reported experience in primary care
Bibliographic record
Abstract
Context The Quadruple Aim is being used to evaluate the impact of Ontario Health Teams, a new model of integrated care in the province of Ontario, Canada, with improved patient experience as one of the core aims. While many tools have been developed to measure both patient-reported experience (PREM) and patient-reported outcomes (PROM), little has been done to routinely implement these within primary and community care. Objective: Describe the routine implementation of a PREM in primary care. Understand how end-users incorporate patient experience data into routine use. Study Design and Analysis: A multiple mixed methods case study design. The Consolidated Framework for Implementation Research and Process Design informed data collection across the five major domains. Two sets of focus groups were completed with each of the four cases to understand the unique experiences of routine PREM collection. Within and across case analysis was used with descriptive statistics for quantitative survey data and thematic analysis for qualitative data. Setting: Three cases were interprofessional primary care clinics and one case was a Public Health organization. All cases were located in one Ontario Health Team in the province of Ontario, Canada. Population Studied: Each case included patients attending clinic appointments and 2-3 decision makers from each case. Intervention/Instrument: Real time collection of patient experience data. Outcome Measures: PREM with three domains: encounter experience, health and well-being and demographics. Results: A total of 1222 patients completed the survey over 5 months. Different mechanisms were used to deploy the PREM, including weekly emails to patients, tablets in waiting rooms and posted QR codes. The overall patient experience of the appointment varied across cases;99% of patients in one case rated their experience as very good or excellent to 87% in another case. Patients in the three primary care sites were less likely to report their health care needs were addressed (87%) than those in the Public Health sites (98%). Clinics received individualized reports on a weekly, bi-weekly or monthly basis, depending on their stated preferences. Themes from the focus groups included using the data for ongoing QI, boosting moral, resources needed for ongoing use. Conclusions: The study highlights the variation in how PREMs are deployed and used in primary care, with a range of patient experiences.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".