Using Patient and Family Engagement Strategies to Improve Outcomes of Health Information Technology Initiatives: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Many health care organizations around the world have implemented health information technologies (ITs) to enhance health service efficiency, effectiveness, and safety. Studies have demonstrated that promising outcomes of health IT initiatives can be obtained when patients and family members participate and engage in the adoption, use, and evaluation of these technologies. Despite knowing this, there is a lack of health care organizations using patient and family engagement strategies to enhance the use and adoption of health ITs, specifically. OBJECTIVE: This study aimed to answer the following three research questions (RQs): (1) what current frameworks or theories have been used to guide patient and family engagement in health IT adoption, use, implementation, selection, and evaluation?, (2) what studies have been done on patient and family engagement strategies in health IT adoption, use, implementation, selection, and evaluation?, and (3) what patient and family engagement frameworks, studies, or resources identified in the literature can be applied to health IT adoption, use, implementation, selection, and evaluation? METHODS: This scoping review used a five-step framework developed by Arksey and O'Malley and adapted by Levac et al. These steps include the following: (1) identifying the RQ, (2) identifying relevant studies, (3) selecting studies, (4) charting relevant data, and (5) summarizing and reporting the result. Retrieved academic and grey literature records were evaluated using a literature review software based on inclusion and exclusion criteria by two independent reviewers. If consensus was not achieved, two reviewers would resolve conflicts by discussion. Research findings and strategies were extracted from the studies and summarized in data tables. RESULTS: A total of 35 academic articles and 23 gray literature documents met the inclusion criteria. In total, 20 of the 35 included studies have been published since 2017. Frameworks found include the patient engagement framework developed by Healthcare Information and Management Systems Society and the patient and family engagement framework proposed by Carman et al. Effective strategies include providing patients with clear expectations and responsibilities and providing reimbursement for time and travel. The gray literature sources outlined key considerations for planning and supporting engagement initiatives such as providing patients with professional development opportunities, and embedding patients in existing governance structures. CONCLUSIONS: Several studies have reported their findings regarding successful strategies to engage patients and family members in health IT initiatives and the positive impact that can emerge when patients and family members are engaged in such initiatives in an effective manner. Currently, no framework has consolidated all of the key strategies and considerations that were found in this review to guide health care organizations when engaging patients and family members in a health IT-specific project or initiative. Further research to evaluate and validate the existing strategies would be of value.
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 | 0,093 | 0,246 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,045 | 0,035 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,009 | 0,010 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,003 |
| 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 ».