Advancing PROMs for health system use in Canada and beyond
Bibliographic record
Abstract
PROMs are essential to delivering patient-centred health care, and when applied routinely they can enhance communication between patients and providers, inform decisions for value-based health system improvements and improve overall patient care experiences and outcomes. The use of patient-reported outcome measures (PROMs) across Canada varies across provinces and territories, partly because of differences in health care delivery models across these jurisdictions. A national program that coordinates uses of PROMs is needed to ensure that this information is comparable across jurisdictions. This commentary provides a summary look at the development of national PROMs data standards and reporting for hip and knee replacement surgery, including the selection of survey tools, building consensus, developing and promoting standards, and reporting on the results nationally and internationally as well as outlining recent learnings from regional implementation of data standards. In 2017, the Canadian Institute for Health Information published national PROMs data collection standards for hip and knee arthroplasty that included guidelines for survey time points, the minimum data set and PROMs instruments. This broad-scale PROMs collection initiative had stakeholder engagement and support from multiple levels within the health system, including administrators, clinic managers, patients, and health system decision-makers. Learnings from regional implementation of the standards demonstrated the importance of assessing existing infrastructure and information technology requirements, mapping clinical workflows, planning for human and information technology resources, navigating local legislation and hospital policies and ensuring data linkage capabilities. This initiative showed the need for a common regional approach for PROMs collection to be efficient and effective. The learnings from implementation of the national Canadian PROMs program for hip and knee arthroplasty can be used as an example for other jurisdictions and clinical areas such as renal care and mental health. Common data standards allow for secondary use of this data that is valuable for reporting and informing policy and guidelines as well as meeting care delivery goals to further the shift in health care systems becoming more patient-centred to improve the quality-of-life of patients.
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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 | 0.102 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".