Developing a Patient Reported Experience Measure (PREM) to assess patients’ experiences with care transitions and integration
Notice bibliographique
Résumé
Background: Care transitions (CTs) across the care continuum (e.g., hospital to primary care/community), especially for those with complex care needs and multimorbidity, is an important focus for improvement. Complex patients in particular tend to be at higher risk for adverse events such as medication errors and rehospitalization due to poor discharge coordination and communication. Primary care plays a significant role in improving coordination and communication to support successful transitions in care. An ongoing study in Alberta, Canada called A DiseAse-Inclusive Pathway for Transitions in Care (ADAPT) focuses on integrating care by collaborating with Primary Care Providers to enhance CTs. Alberta Health Service (AHS) Primary Health Care Integration Network (PHCIN) has been leading the development of the Home to Hospital to Home (H2H2H) Transitions Guideline for several years. A major aspect of this initiative is evaluating how patients experience transitions from hospital to home. There are few validated patient reported experience measures (PREMs) that capture multiple transition points from discharge preparation, to home, to primary care. The objective of this work was to develop a PREM to capture patients experience of care while transitioning across multiple settings, with a focus on integration of care across. Approach: Methods to achieve our objective included a literature review, identification of core domains and questions across clinical settings, and then pre-testing the instrument with content experts and patients/caregivers with lived experience to establish content and face validity. After iterative pre-testing and revisions, we plan to pilot the newly developed PREM in one site prior to broader application. Psychometric testing of the PREM will be done as part of the larger study which will also explore strategies to bolster response rates from patients involved in this study aimed at improving CTs for adult patients with diverse chronic conditions and better integrating their care. Results: The literature review identified 3 potentially relevant PREM instruments. Criteria for inclusion in the review were an adult patient population, and relevance to transitions in care between hospital and primary care settings. Core domains of interest were superimposed onto PREM items, including patient knowledge, self-efficacy, care preference alignment, integration/coordination, and satisfaction throughout CTs. Existing PREMs were limited in capturing the patient experience as they transitioned through different levels of care. Items for the new PREM were developed to ensure representation of core domains of interest for CTs from hospital to home, including integration with primary care in the post-discharge period. The PREM is currently undergoing pre-testing with patient advisors (n = 5) and content experts (n = 5). Once pre-testing and revisions are complete the PREM will be applied in a small pilot, and then in the implementation evaluation of a provincial transitions in care initiative, i.e., the H2H2H Guideline. Implications: Creating an instrument that captures patient experiences as they move between acute and primary care, with a focus on integrating care will be key to gaining understanding and improving CTs. The development of a PREM to evaluate across levels of care will better inform various interest groups on how different system changes impact the patient experience. This approach to a PREM instrument also reinforces the importance of viewing the patient experience in a more integrated manner rather than in components.There are few validated PREMs in Canada, or elsewhere, that capture patient experiences throughout the transition process across the continuum of care from hospital to home. The PREM we are developing would be applicable to applied research and learning health organizations across Canada given the current gap in this area. The PREM will provide robust evidence to assess patient experience metrics to support quality improvement work and enhance integration of 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 | 0,010 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».