Electronic Clinical Decision Support System for Stroke Risk Screening in Patients With Atrial Fibrillation in Mental Health Care: Mixed Methods Study
Notice bibliographique
Résumé
Background: Electronic clinical decision support systems (eCDSSs) are key to the digital transformation of health care. Despite their growing adoption, little is known about the perspectives of mental health clinicians on the implementation of eCDSS to assist them in managing physical health conditions within mental health care settings. Objective: This study aimed to explore how clinicians in older adult mental health services manage stroke risk in patients with atrial fibrillation (AF) and comorbid serious mental illness who are admitted to the hospital under their care. It also sought to examine clinicians' views on the potential role of an eCDSS in enhancing stroke risk assessment and management. Methods: A cross-sectional mixed methods study was conducted between March and May 2023 in 3 inpatient wards for mental health of older adults at South London and Maudsley NHS (National Health Service) Foundation Trust. Health care professionals, including psychiatrists and pharmacists, participated in a web-based survey and individual semistructured interviews. Ethical approval and informed consent were obtained. A descriptive analysis was conducted on the survey data, while interview data were analyzed thematically using an inductive approach. Results: In total, 10 clinicians participated in the study. Thematic analysis revealed 2 primary themes. First, clinicians reported significant challenges in clinical practice, including difficulties accessing patient medical histories, limited expertise in managing physical health conditions, fragmented care pathways, and the impact of mental health symptoms such as psychotic beliefs on stroke prevention. Second, clinicians identified strategies to improve practice, such as embedding alerts in electronic records, establishing clear organizational policies, and providing tailored training on AF-related stroke management. Clinicians recognized the potential of an eCDSS to enhance clinical effectiveness, improve the identification of high-risk patients, ensure safer and more consistent care, and save time. However, they expressed concerns about potential risks, including rigidity in decision-making, overreliance on the tool, false positives, reduced critical thinking, annoyance, and increased workload. Conclusions: This study highlights the challenges and opportunities in managing AF-related stroke risk in mental health settings. While clinicians acknowledged the potential of an eCDSS to improve care quality and efficiency, addressing concerns about its design and implementation is essential. These insights can inform the development of eCDSS tools that effectively balance benefits with user needs, ultimately improving patient outcomes in mental health services.
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 | 0,015 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 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 ».