Validation of a real-time patient-reported experience measurement tool for cancer patients in Ontario.
Bibliographic record
Abstract
174 Background: Cancer Care Ontario (CCO) organizes and ensures quality cancer care for 16 million residents in Ontario. CCO's goal is to create a tool to enable reporting of the patient experience in real time. The study objective was to develop a validated real-time (close to or at point of care) patient reported experience measurement (PREM) tool to drive quality improvement, advance system planning and ensure cost effective resource allocation based on patients’ needs, wants and preferences in ambulatory oncology treatment phase. Methods: Consisting of 28 questions – 18 core questions; 2 open-ended responses; and 8 demographic questions the tool underwent cognitive testing with patient family advisors and was validated using a pilot at 6 cancer centres from April to August, 2015. It was administered using several delivery modes: tablets, patient portals, post cards and paper. The patient journey in visiting centres was conceptualized as containing the steps or domains of interest: arrival, appointment/consultation and overall experience. To validate the patient experience conceptual model, an exploratory factor analysis was performed and compared to CCO’s 8 domain patient experience model. Reliability and validity of the tool was tested using Cronbach’s alpha and multiple variable regression analysis in partnership with Ipsos Reid. Results: 557 responses (496 electronic and 61 paper) formed the basis of the analysis. The tool was shown to be both valid and reliable - an overall adjusted R2 value of 64% from multiple variable regression on overall patient experience; and an overall Cronbach alpha value of 0.83, respectively. Individual Cronbach alpha values ranged from: 0.69 for “appointment/consultation”, 0.55 for “overall experience” and 0.24 for patient “arrival.” Conclusions: Statistical analysis results were used to produce a final validated tool for deployment to systematically capture PREMs through an electronic system in Ontario, Canada in 2016.To our knowledge this is the first of its kind. Collection of real time PREMS will ensure local and system level planning and quality improvement in the cancer system are rooted in what matters most to patients and their families.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".