Focused Bedside Education May Improve Engagement of Hospitalized Patients with Their Patient Portals
Notice bibliographique
Résumé
A Review of: Greysen, S.R., Harrison, J.D., Rareshide, C., Magan, Y., Seghal, N., Rosenthal, J., Jacolbia, R., & Auerbach, A.D. (2018). A randomized controlled trial to improve engagement of hospitalized patients with their patient portals. Journal of the American Medical Informatics Association, 25(12), 1626-1633. https://doi.org/10.1093/jamia/ocy125 Abstract Objectives – To study hospitalized patients who were provided with tablet computers and the extent to which having access to these computers increased their patient portal engagement during hospitalization and following their discharge. Design – Prospective, randomized controlled trial (RCT) within a larger, observational study of patient engagement in discharge planning. Setting – A large, academic medical centre in the Western United States of America. Subjects – Of a total of 250 potential subjects from a larger observational study, 137 declined to participate in this one; of the remaining 113 subjects, 16 were unable to access the patient portal, leaving 97 adult (18 years of age or older) patients in the final group. All subjects (50 intervention and 47 control) were randomized but not blinded, had been admitted to medical service, and spoke English. In addition, all participants were supplied with tablet computers for one day during their inpatient stay and were provided with limited assistance to the portal registration and login process as needed. They were also required to have access to a tablet or home computer when discharged. Methods – The intervention group participants received focused bedside structured education by trained research assistants (RAs) who demonstrated portal key functions and explained the importance of these functions for their upcoming transition to post-discharge care. Following enrolment and consent, RAs administered a brief pre-study survey to assess baseline technology use. Then, at the end of the observation day, the RAs performed a debrief interview in which participants were asked to demonstrate their ability to perform key portal tasks. The RAs recorded which tasks were accomplished or if the RAs had provided assistance. Patient demographics and clinical information were obtained from the Electronic Health Record (EHR). Main results – Of the 97 patients who were enrolled in the RCT, 57% logged into their portals at least once within seven days of their discharge. The mean number of logins and specific portal tasks performed was higher for the intervention group than for the control group. In addition, while in the hospital, the intervention group was better able to log in and navigate the portal. Only one specific portal task reached statistical significance—the use of the tab for viewing the messaging interaction with the provider. The time needed to deliver the intervention was brief—less than 15 minutes for 80% of participants. The intervention group’s overall satisfaction with the bedside tablet to access the portal was high. Conclusion – Data analysis revealed that the bedside tablet educational intervention succeeded in increasing patient engagement in the use of the patient portal, both during hospitalization and following discharge. As the interest and demand for patient access to EHRs increases among patients, caregivers, and healthcare providers, more rigorous studies will be needed to guide the implementation of patient portals during and after hospitalization.
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 | 0,003 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 ».