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Enregistrement W4413358346 · doi:10.5334/ijic.nacic24169

Integrating Patient Reported Data into Primary Care Networks in Saskatchewan

2025· article· en· W4413358346 sur OpenAlexaboutno aff
Chris Plishka, Sarah Fang, Hazel Williams-Roberts, Lorenzo Bacchetto, Laura Beauchesne, Lisa Bradford, Johann Engelke, Trevor Tessier, Maureen Kachor, C Joseph Cross, Faye Hoium, Hercule Bunsana Yimbi, Meriç Osman, Tay Spock, Tracey Carr

Notice bibliographique

RevueInternational Journal of Integrated Care · 2025
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueHealthcare Systems and Technology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPrimary careMedicineNursingFamily medicine

Résumé

récupéré en direct d'OpenAlex

To ensure patient priorities are driving health system decision making, a team co-led by patient and family partners developed and implemented systems to collect patient experience data in four Saskatchewan health networks. The Saskatchewan Health Authority (SHA) is committed to collecting People-Centred Measures (PCMs) at all levels of the health system. PCMs focus on measures that matter to patients. PCMs include Patient Reported Experience Measures (PREMs), Patient Reported Outcome Measures (PROMs) and qualitative ways of hearing the voice of patients. Work was completed to begin collecting and using PCMs in four of the province primary care networks. Primary care networks connect teams of health-care professionals and community partners within a given geographic area to better meet the needs of the people they serve; they allow the SHA to better organize services and resources internally to deliver more reliable and consistent team‐based care as close to home as possible. To begin collecting PCM data, a PCM Implementation team that included primary care directors, patient and family partners, SHA support staff and academic researchers: ) established a team charter outlining our concensus decision making model, 2) developed a set of survey questions that captured important aspects of care that could inform planning and improvement, 3) used these questions to create an online survey, 4) tested the survey with home care clients and continuing care aids, 5) ensured all questions were written in plain language, 6) tailored survey distribution plans for each network to collect a representative sample of each network, 7) presented data in meaningful way to patients, community members and network directors and 8) made recommendations regarding spread and scale of the processes. Results included adoption of a consensus decision making process, creation of survey that collected meaningful data in a way that was accessible to patients and clients, the creation of network specific distribution plans, the collection of PCMs, the identification of common themes and creation of knowledge translation materials for each network. Different distribution strategies were used to varying degrees in each primary care network. These included: social media, traditional media, collaboration with community organizations, promotion in health facility using printed material and a focus on phone interviews. Emphasizing different strategies led to different response rates between networks. None resulted in a strong response from under-recognized populations. Survey responses for each network were summarized using frequencies and themes identified for open-ended responses. Themes included: access to care, continuity of care, interpersonal processes of care, and experiences of discrimination. This data was discussed and interpreted with the team to identify opportunities for improvement. Work is currently underway to action those opportunities. Experience collecting data in these networks is informing future planning around PCM collection. This project will inform future distribution strategies, network-specific quality improvement initiatives, integrated knowledge translation processes and plans for scale and spread. Quality improvement initiatives will be presented in a separate abstract. Responses resulting from different distribution strategies will help inform how data is collected and indicates the need to utilize strategies beyond surveys to hear from under-recognized populations. Network staff found the data reflected their experiences with care delivery; validating many quality improvement initiatives that are currently underway. The team is developing -page summaries indicating what we did what we heard and what we do to share back to the public. The integrated knowledge translation strategy and the resulting commitment to the project are seen as important steps to collecting actionable data and similar processes are recommended moving forward. Finally, the initiative as a whole, is being reviewed at leadership tables and will inform strategies for expansion across all health networks.

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)0,006
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,098
Score d'incertitude au seuil0,196

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,016
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,005
Études des sciences et des technologies0,0030,001
Communication savante0,0020,001
Science ouverte0,0020,003
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,277
Écart entre enseignants0,262 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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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