Improving the management of Acute Myocardial Infarctions: Exploring the barriers and facilitators to implementing a Smartphone Application for physicians (Preprint)
Notice bibliographique
Résumé
BACKGROUND The delivery of timely and appropriate care is crucial for survival in patients experiencing ST-elevation myocardial infarction (STEMI). Efficient communication and exchange of test results between the referring emergency medicine (EM) physician or Emergency Medical Service (EMS) paramedic and the interventional cardiologist (IC) is essential to achieving timely care. In many communities, sharing of some information crucial to decision making, such as electrocardiograms (ECG), relies on using fax or text message. The SMART AMI App was developed to streamline communication and ensure that information is shared in a secure manner. The application is simple to use, privacy-compliant, and allows for rapid ECG sharing between healthcare providers, enabling timely decision-making. OBJECTIVE This paper details the results of targeted pre-implementation surveys to establish the barriers and enablers of using a smartphone application to transmit ECG images among ICs, EM physicians and EMS paramedics to help tailor implementation interventions. METHODS To assess the proposed acceptability and uptake of the application, pre-implementation surveys were disseminated to ICs, EM physicians and EMS paramedics in one region of Ontario, Canada. Questions were generated based on selected components of the Consolidated Framework for Implementation Research, results from a pilot study carried out at a regional hospital where the SMART-AMI App was previously implemented, and predicted barriers based on expert guidance. The pre-implementation surveys consisted of both 7-point Likert scale questions (1=strongly disagree and 7=strongly agree) and open-ended questions. Open-ended data was extracted verbatim and analyzed using an inductive qualitative approach, with transcripts coded into descriptive qualitative codes and then collapsed into themes. RESULTS Uptake of the survey was acceptable, with 9 of the invited 10 ICs, 51 of the invited 223 EM physicians, and 93 of the invited 1138 EMS paramedics responding. Survey findings demonstrated a need for an App, as all groups recognized that current practices for sharing ECGs allowed room for improvement, accepting that fax can be inconvenient and text messages may not be secure. When asked whether there was a need for a smartphone application to transmit ECGs, ICs (M=6.67, SD=0.50), EM physicians (M=5.57, SD=1.30) and EMS paramedics (M=5.79, SD=1.45) consistently agreed. Commonly reported barriers were concerns over technological challenges, privacy issues, and cell phone reception strength. Through identification of the barriers in each stakeholder group, implementation strategies were developed that facilitated the scale-up of this system-change intervention. CONCLUSIONS Results from the three online pre-implementation surveys to identify key barriers and enablers to implementation of the App helped inform the selection of tailored implementation strategies to support roll-out of the App across the health region. The surveys identified key barriers around technology, privacy concerns, and access to required WiFi that needed to be addressed during App implementation to facilitate uptake and use. Results from the surveys, and ongoing evaluation of effectiveness, are informing expansion of the App intervention to local ambulance services and other health regions. CLINICALTRIAL https://clinicaltrials.gov/study/NCT05290389
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 | 0,008 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».