Integrating shared decision making into primary care: Lessons learned from a multi-centre feasibility randomized controlled trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND We previously developed MyDiabetesPlan, an evidence-based, online, interactive patient decision-aid to facilitate patient-centred, diabetes-specific goal-setting and action-planning, using shared decision making (SDM) with interprofessional (IP) healthcare teams. OBJECTIVE The aim of this study is to assess the feasibility of (1) integrating MyDiabetesPlan into routine workflows in IP primary care clinics, and (2) conducting a cluster randomized controlled trial (RCT). METHODS We conducted a pilot cluster-RCT in 10 IP primary care clinics with patients living with diabetes and 2+ other comorbidities; half of the clinics were assigned to the MyDiabetesPlan intervention and the remainder were assigned to usual care. For Objective 1, we used RCT conduct logs and financial account summaries to assess recruitment, retention metrics, and resource use. For Objective 2, we used RCT conduct logs and website usage logs to assess intervention fidelity and resource usage. We used audiotapes of clinical encounters in the intervention groups to identify barriers and facilitators to integration of MyDiabetesPlan into clinical care across the IP team. RESULTS Objective 1: 1597 potentially eligible patients were identified through electronic medical record-based searches, of which 1113 patients met eligibility criteria upon detailed chart review. A total of 425 patients were randomly selected; of these, 213 were able to participate and were allocated (intervention: n=102; control: n=111), for a recruitment rate of 50.1%. 151 patients completed the study, for a retention rate of 70.9%. A total of 5745 personnel-hours and $6104 CAD were attributed to recruitment and retention activities. Objective 2: A total of 179 appointments occurred (out of a total of 204 expected appointments - 2 per participant over the 12-month study period; 87.7%). Forty (36%), 25 (23%) and 32 (29%) patients completed MyDiabetesPlan at least twice, once, and zero times respectively. Mean time for completion of MyDiabetesPlan by the clinician and the patient during initial appointments was 37 minutes. From the clinical encounter transcripts, we identified diverse strategies used by health care providers and patients to integrate MyDiabetesPlan into the appointment, characterized by rapport-building and individualization. Barriers to use included MyDiabetesPlan-related factors (e.g. limited selection of potential diabetes management strategies), clinician-related factors (e.g. discomfort with asking certain questions), and patient-related factors (e.g. computer literacy). CONCLUSIONS We evaluated the feasibility of an IPSDM approach using decision aids to help establish treatment priorities in patients with diabetes and found that it would be feasible. A total of 151 (70.9%) patients were retained for 12 months, which required 38 personnel hours and $40.42 CAD per participant who completed the study. Lower than expected numbers of diabetes-specific appointments were observed, and only 39% of patients completed MyDiabetesPlan twice. Addressing facilitators and barriers identified in this study will improve feasibility and promote more complete and seamless integration into clinical care. CLINICALTRIAL Clinicaltrials.gov Identifier: NCT02379078 Date of Registration: February 11, 2015
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 | 0,138 | 0,192 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,006 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».