Selection of patient-reported outcome measures (PROMs) for use in health systems
Bibliographic record
Abstract
Many healthcare systems around the world have been increasingly using patient-reported outcome measures (PROMs) in routine outcome measurement to enhance patient-centered care and incorporate the patient's perspective in health system performance evaluation. One of the key steps in using PROMs in health systems is selecting the appropriate measure(s) to serve the purpose and context of measurement. However, the availability of many PROMs makes this choice rather challenging. Our aim was to provide an integrated approach for PROM(s) selection for use by end-users in health systems.The proposed approach was based on relevant literature and existing guidebooks that addressed PROMs selection in various areas and for various purposes, as well as on our experience working with many health system users of PROMs in Canada. The proposed approach includes the following steps: (1) Establish PROMs selection committee; (2) Identify the focus, scope, and type of PROM measurement; (3) Identify potential PROM(s); (4) Review practical considerations for each of the identified PROMs; (5) Review measurement properties of shortlisted PROMs; (6) Review patient acceptance of shortlisted PROMs; (7) Recommend a PROM(s); and (8) Pilot the selected PROM(s). The selection of appropriate PROMs is one step in the successful implementation of PROMs within health systems, albeit, an essential step. We provide guidance for the selection of PROMs to satisfy all potential usages at the micro (patient-clinician), meso (organization), and macro (system) levels within the health system. Selecting PROMs that satisfy all these purposes is essential to ensure continuity and standardization of measurement over time. This is an iterative process and users should consider all the available information from all presented steps in selecting PROMs. Each of these considerations has a different weight in diverse clinical contexts and settings with various types of patients and resources.
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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 | 0.381 | 0.492 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".