Using mHealth to Increase the Reach of Local Guidance to Health Professionals as Part of an Institutional Response Plan to the COVID-19 Outbreak: Usage Analysis Study
Notice bibliographique
Résumé
BACKGROUND: The ongoing coronavirus disease (COVID-19) pandemic forced health jurisdictions worldwide to significantly restructure and reorganize their medical activities. In response to the rapidly evolving body of evidence, a solid communication strategy is needed to increase the reach of and adherence to locally drafted and validated guidance to aide medical staff with COVID-19-related clinical decisions. OBJECTIVE: We present a usage analysis of a dedicated mobile health (mHealth) platform as part of an institutional knowledge dissemination strategy of COVID-19-related guidance to all health care workers (HCWs) in a large academic hospital. METHODS: A multidisciplinary team of experts drafted local guidance related to COVID-19. In total, 60 documents and 17 external links were made available through the platform. Documents were disseminated using a recently deployed mHealth platform for HCWs. Targeted dissemination of COVID-19-related content began on March 22, 2020. Using a third-party statistics tool, data concerning user activity and content use was anonymously collected. A quantitative analysis of user activity was performed over a 4-month period, separated into 3 periods: 2 months before (Period A), 2 weeks after (Period B), and 6 weeks following (Period C) targeted dissemination. Regional epidemiological data (daily new COVID-19 cases and total COVID-19-related hospitalizations) was extracted from an official registry. RESULTS: During the study period, the platform was downloaded by 1233 new users. Consequently, the total number of users increased from 1766 users before Period A to a total of 2999 users at the end of Period C. We observed 27,046 document views, of which 12,728 (47.1%) were COVID-19-related. The highest increase in activity occurred in Period B, rapidly following targeted dissemination, with 7740 COVID-19-related content views, representing 71.2% of total content views within the abovementioned period and 550 daily views of COVID-19-related documents. Total documents consulted per day increased from 117 (IQR 74-160) to 657 (IQR 481-1051), P<.001. This increase in activity followed the epidemiological curbing of newly diagnosed COVID-19 cases, which peaked during Period B. Total active devices doubled from 684 to 1400, daily user activity increased fourfold, and the number of active devices rose from 53 (IQR 40-70) to 210 (IQR 167-297), P<.001. In addition, the number of sessions per day rose from 166 (IQR 110-246) to 704 (IQR 517-1028), P<.001. A persistent but reduced increase in total documents consulted per day (172 [IQR 131-251] versus 117 [IQR 74-160], P<.001) and active devices (71 [IQR 64-89] versus 53 [IQR 40-70]) was observed in Period C compared to Period A, while only 29.8% of the content accessed was COVID-19-related. After targeted dissemination, an immediate increase in activity was observed after push notifications were sent to users. CONCLUSIONS: The use of an mHealth solution to disseminate time-sensitive medical knowledge seemed to be an effective solution to increase the reach of validated content to a targeted audience.
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 | 0,016 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».