How patients define success for integrated care: How the University Health Network identifies iterative improvement priorities
Notice bibliographique
Résumé
Background: While many standardized surveys exist to measure patient experience, they are not designed to assess unique features of integrated care (IC) programs including transitions from hospital to community, satisfaction with homecare, or resources and supports provided post-discharge. The aim was to have patients lead efforts to develop a set of quality indicators for routine monitoring of patient experience in the University Health Network (UHN) integrated models of care in Toronto, Ontario, Canada. The output would be used to evaluate efforts and prioritize ongoing improvement to continue to meet patient needs. Population: We invited 29 stakeholders to participate in this study between April-July 2022. Twenty-three (79%) were female, 14% identified as a person with a disability, and 28% identified as a person of colour. Nearly half of all participants were patients or caregivers (42%), followed by administration/clinicians (58%). Engagement: This work was carried out in partnership with patients and caregivers. We worked collaboratively with the UHN Patient Experience Team to ensure materials and processes were effectively designed for engagement. Methods: We used the RAND-modified Delphi method, an approach combining scientific literature and expertise of stakeholders, to reach consensus on an agreed-upon set of indicators to measure patient experience. The study consisted of two surveys and a virtual meeting. In survey #1, participants answered demographic questions and rated 45 patient-reported experience measures (PREMs) on whether they felt the indicator was reliable, necessary, and actionable, and also provided written feedback on each. We averaged responses and feedback to rank indicators; those with above-average ratings were included in survey #2 where respondents identified if each indicator should be included as a key performance metric. All results were shared and discussed at the virtual meeting. Results: Among the 29 stakeholders surveyed, 27(93%), 25(86%) participated in survey #1 and #2, respectively. The average rating for indicators in survey #1 was 79%, resulting in 23 indicators proceeding to survey #2. In 71.1% of indicators from survey #1 there was full agreement, meaning that both patients/caregivers and administration/clinicians rated above or below 79% (i.e., there was agreement to keep or remove the indicator). The remaining 29% of indicators had half agreement between patients/caregivers and administration/clinicians. Divergence of opinion mostly occurred for indicators that assessed the patient’s emotional well-being (e.g., addressing anxieties, fears, worries). The final set of indicators covers pre-hospital admission through the post-discharge period and assesses domains such as informational continuity, person-centered care, timeliness of care, effectiveness of resources and supports, and efficacy of knowledge translation. Conclusions: After 2 survey rounds and a virtual meeting participants reached consensus on 13 indicators to measure patient experience. This is the first set of PREMs co-designed by patients and caregivers with a focus on IC applications in Canada. Lessons Learned: Patients and caregivers rated indicators that measure domains of emotional support and reassurance higher than administration/clinicians, highlighting the importance of sub-group analysis in designing patient experience measures. Next Steps: Following a successful pilot in the Division of Orthopedics, these indicators will be implemented in all UHN IC programs.
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 | 0,161 | 0,212 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,008 | 0,005 |
| Études des sciences et des technologies | 0,019 | 0,020 |
| Communication savante | 0,035 | 0,039 |
| Science ouverte | 0,007 | 0,041 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».