Integrating Prevention into Routine Clinical Workflows in Alberta, Canada through a Learning Health System Approach: Early Lessons Learned
Notice bibliographique
Résumé
Background: We present lessons from the Integrating Prevention into Health (IPiC-Health) initiative as a rapid LHS in complex clinical settings, aimed at improving the population health and well-being through preventive care. Approach: The IPiC-health project builds a rapid LHS for preventive care, leveraging health information technology to promote measurable change for improving population health and wellness; integrating screening, brief intervention and referral (SBIR) for modifiable risk factors into Connect Care - Alberta province-wide Electronic Health Record (EHR). This interventional research project utilizes mixed methods (pragmatic trial and qualitative assessment) for implementation and effectiveness research. As an important part of the LHS, we co-create and integrate clinical workflows, create rapid feedback mechanisms for improving care, and study effectiveness of integrating SBIR on patient outcomes, while capturing provider and patient perspectives. Results: We identified and engaged key partners, multi-disciplinary experts and site champions for co-designing clinical workflows, including Alberta Health Services leadership, clinical and academic sites. Leadership at eight clinical sites (5 ambulatory, 3 inpatient) agreed to the importance and implementation of SBIR.Embedded Research Coordinators/staff played a critical role in formalizing assessing readiness, patient recruitment, creating champions and clinical workflows in close consultation with clinical teams. Readiness assessments helped tailor training and support SBIR integration into clinical and EHR workflows. Control group recruitment followed with 238 participants; 644 high or medium risk for at least one risk factor (smoking, alcohol use, physical inactivity); recruited by December 2023 at 3 sites, while currently 2 sites have started the implementation phase.Towards operationalizing the LHS, we employ mixed methods (EHR data, patient and clinician semi-structured interviews, clinician observation), to identify challenges, and create feedback reports for reflection, motivation, and improvement at clinical sites. Identified challenges and barriers are recorded and resolved using the Consolidated Framework for Implementation Research.Important lessons learnt during the project initial phase include limitations in the current EHR offering, such as fragmented documentation for preventive care; tools not aligned to care delivery in Alberta; different interfaces for different providers; problems accessing the SBIR domains for charting; lack of or duplicated fields leading to inconsistent data recording; and lack of linkages to referral related provincial programs, such as Alberta Quits. Implications: Preventive care is an oft-overlooked aspect within the care continuum. We are co-creating standardized clinical workflow at sites for integrating SBIR in routine care, while offering on-ground support to clinical teams. This will be helpful towards promoting longer term sustainability for improving SBIR in routine care. We are also initiating patient surveys for gauging the effectiveness of SBIR in behavior change.While providers view SBIR for modifiable risk factors as important, Connect Care workflow optimization, motivation and continued education are key concerns for longer-term uptake and use of the LHS for preventive 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 | 0,020 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,005 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».