How patient-reported outcomes and experience measures (PROMs and PREMs) are implemented in healthcare professional and patient organizations? An environmental scan
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are becoming essential parts of a learning health system, and using these measures is a promising approach for value-based healthcare. However, evidence regarding healthcare professional and patient organizations' knowledge, use and perception of PROMs and PREMs is lacking. OBJECTIVES: The objectives of the study were to: 1- Describe the current knowledge and use of PROMs and PREMs by healthcare professional and patient organizations, 2- Describe the determinants of PROMs and PREMs implementation according to healthcare professional and patient organizations. METHODS: We conducted an environmental scan using semi-structured interviews with representatives from healthcare professional and patient organizations. Interviews were recorded and live coded based on the Franklin framework. We used inductive and deductive thematic analysis to extract information about the main themes addressed during the interview (awareness of PROMs and PREMs, examples of implementation and use of PROMs and PREMs, tools used, vision for future implementation, barriers and facilitators to implementation and the best way to collect PROMs and PREMs data). RESULTS: 63% of healthcare professional organizations (n = 19) and 41% of patient organizations (n = 9) that were contacted agreed to have a representative interviewed. The representatives from both the healthcare professional and patient organizations acknowledged the importance of assessing patients' experience and outcomes. However, they considered the implementation of PROMs and PREMs tools to be scarce within their organizations, in clinical practice and in the education system. Patient organizations were worried that overuse of PROMs and PREMs could lead to depersonalization of practice. Barriers to implementing PROMs and PREMs included lack of awareness of tools, resistance to change and lack of motivation to complete or explain the questionnaire. Barriers also included factors such as lack of financial, technological and human resources and issues with integration of data and inconsistency of digital platforms. CONCLUSIONS: This environmental scan revealed a lack of awareness of tools by healthcare professional and patient organizations' representatives and limited implementation. Adequate training, technological integration, and demonstration of PROMs and PREMs benefits to foster broader adoption in clinical and organizational settings is dearly needed. Addressing these challenges is essential for enhancing value-based care.
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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 | 0.049 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".