Systematically implementing an electronic platform to improve patient experience measurement in Ontario, Canada.
Bibliographic record
Abstract
111 Background: Cancer Care Ontario organizes and ensures quality cancer care for 13.5 million residents in Ontario, Canada and is systematically deploying a web-based tool, called Electronic Patient Reported Experience Measures (ePREM), through a touch-screen platform previously deployed within Regional Cancer Centres (RCCs). ePREM has been used to disseminate the first patient reported experience measure (PREM), called Your Voice Matters (YVM). YVM will be collected on all patients in Ontario undergoing cancer treatment in real time, creating the largest known, linkable patient experience dataset. Methods: Implementation feasibility and readiness was assessed across all 14 Regional Cancer Programs through a provincial assessment which informed the phased implementation. Implementation was rooted in a change management framework: clinical and administrative champions, available resources, existing technical environment and competing program priorities. In March 2016 a four-wave implementation was initiated controlling for readiness and centre volumes, with a focus on extensive stakeholder engagement, tool training, launch and operational support. To date, ePREM is fully operational in 10 of 15 RCCs. Results: Provincial deployment was 66% complete in October 2016. By March 2017 full provincial implementation will be complete. Successful implementation has been directly linked to an adaptive design within the implementation and change management framework as a four-wave roll out. Key factors included: multi-faceted communications with centre leadership, and implementation teams. During this initial data stabilization phase, there were 9,266 completed surveys submitted and 10,932 partial survey responses submitted. Conclusions: PREMs are appropriate quality improvement indicators for cancer patients within the treatment phase. ePREMs enables systematic, linkable collection of real time PREMs to improve the patient experience in Ontario and contribute to the planning of new initiatives. Upon full provincial implementation ePREMs will allow for the largest known dataset of PREMs. Successful implementation has been directly linked to an adaptive, four-wave approach.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".