The Role of Clinician-Developed Applications in Promoting Adherence to Evidence-Based Guidelines: Pilot Study
Notice bibliographique
Résumé
Background: Computerized clinical decision support systems (CDSS) are increasingly being used in clinical practice to improve health care delivery. Mobile apps are a type of CDSS that are currently being increasingly used, particularly in lifestyle interventions and disease prevention. However, the use of such apps in acute patient care, diagnosis, and management has not been studied to a great extent. The Pathway for Acute Coronary Syndrome Assessment (PACSA) is a set of guidelines developed to standardize the management of suspected acute coronary syndrome across emergency departments in New South Wales, Australia. These guidelines, which risk stratify patients and provide an appropriate management plan, are currently available as PDF documents or physical paper-based PACSA documents. The routine use of these documents and their acceptability among clinicians is uncertain. Presenting the PACSA guidelines on a mobile app in a sequential format may be a more acceptable alternative to the current paper-based PACSA documents. Objective: This study aimed to assess the utility and acceptability of a clinician-developed app modeling the PACSA guidelines as an alternative to the existing paper-based PACSA documents in assessing chest pain presentations to the emergency department. Methods: An app modeling the PACSA guidelines was created using the Research Electronic Data Capture (REDCap) platform by a cardiologist, with a total development time of <3 hours. The app utilizes a sequential design, requiring participants to input patient data in a step-wise fashion to reach the final patient risk stratification. Emergency department doctors were asked to use the app and apply it to two hypothetical patient scenarios. Participants then completed a survey to assess if the PACSA app offered any advantages over the current paper-based PACSA documents. Results: Participants (n=31) ranged from junior doctors to senior physicians. Current clinician adherence to the paper-based PACSA documents was low with 55% (N=17) never using it in their daily practice. Totally, 42% of participants found the PACSA app easier to use compared to the paper-based PACSA documents and 58% reported that the PACSA app was also faster to use. The perceived usefulness of the PACSA app was similar to the perceived usefulness of the paper-based PACSA documents. Conclusions: The PACSA app offers a more efficient and user-friendly alternative to the current paper-based PACSA documents and may promote clinician adherence to evidence-based guidelines. Additional studies with a larger number of participants are required to assess the transferability of the PACSA app to everyday practice. Furthermore, apps are relatively easy to develop using existing online platforms, with the scope for clinicians to develop such apps for other evidence-based guidelines and across different specialties.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».