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Enregistrement W4413365888 · doi:10.5334/ijic.nacic24056

Developing a Patient Reported Experience Measure (PREM) to assess patients’ experiences with care transitions and integration

2025· article· en· W4413365888 sur OpenAlexaboutno aff
Sarah Filiatreault, Jodi Cullum, Ceara Cunningham, Staci Hastings, Judy Seidel, Sara N. Davison

Notice bibliographique

RevueInternational Journal of Integrated Care · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMeasure (data warehouse)MedicineIntegrated careNursingHealth carePsychologyComputer science

Résumé

récupéré en direct d'OpenAlex

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,022
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,054

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,022
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,058
Tête enseignante GPT0,413
Écart entre enseignants0,355 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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