How patient-reported outcomes and experience measures (PROMs and PREMs) are implemented in healthcare professional and patient organizations? An environmental scan
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,049 | 0,107 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,007 |
| Communication savante | 0,004 | 0,007 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».