The use of patient-reported outcome measures in primary care: applications, benefits and challenges
Bibliographic record
Abstract
PROMs use in primary care has expanded from simply describing patient populations to contributing to decision-making, in response to the increasingly complex, ever-changing healthcare environment. In Alberta, primary care is organized into primary care networks (PCNs), where family physicians are grouped geographically and supported by allied health professionals. PCNs implement programs and services in response to local population health needs with frequent evaluation, often incorporating PROMs for this purpose. As PCN programs and services vary greatly across Alberta, so do their use of PROMs. An area of commonality is the use of the EQ-5D-5L instrument; 29 out of 41 PCNs are registered and licensed to use the instrument. It is often administrated by paper, pre- and post-program, and in combination with other specific measures, depending on the program or target population. Some PCNs share programming and therefore outcome measurement, but often the selection, implementation (including training and administration procedures) and evaluation/reporting of PROMs are unique to the PCN. As well, data analysis is largely dependent on the size and capacity of the PCN. Using PROMs for PCN program evaluation supports clinical understanding and complements clinical outcomes. PROMs describe the population attending a program, as well as provide an element of consistency when examining trends across multiple programs or timepoints. This contributes to inquiries and decisions around program development, components, administrative features, resource allocation and delivery. Challenges of PROMs use in primary care include the absence of cohesive data capture technology. This limits data capabilities and presents difficulties with data fidelity, storage, export, and analysis. Additionally, this real-world application lacks a control arm and presents methodological challenges for comparative research purposes. Furthermore, capturing long term patient outcomes poses administrative challenges of multiple follow ups. More research is required into best reporting mechanisms to ensure the data is used to its full potential. To overcome these challenges, leadership and clinician engagement are key. As well, determining consistent PCN PROM reporting requirements will ensure data are comparable across PCNs and contribute to provincial level evaluations, further supporting the movement towards overall health system quality improvement.
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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 | 0.438 | 0.545 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| 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".