Preliminary Analysis of Worldwide Usage Patterns in a Mobile Palliative Care Reference App
Notice bibliographique
Résumé
Background: Fast Facts and Concepts for iOS and Android is the world’s most downloaded point of care mobile reference application for palliative care providers. This free mobile app leverages the Fast Facts and Concepts article repository that was started in 1999 at the End-of-Life/Palliative Education Resource Center at the Medical College of Wisconsin. Our team released the initial iOS version of the app in summer of 2014. Since then, it has been downloaded over 13,000 times. Objective: The purpose of this project is to evaluate and describe user behaviors of palliative care clinicians on a global scale using an analytics layer integrated into Fast Facts and Concepts. Methods: An analytics layer was integrated and disclosed with version 1.0.3; an analytics event is triggered when an article is read or when a search is made. The event, along with anonymous user metadata, was sent to a Web server where it was segmented. Summary statistics were generated using Python scripts and include category weight, article rank, and search term clusters. We evaluated user behavior of the Fast Facts and Concepts app during a 3-month window to better understand the needs of the userbase. Results: Our dataset had 26,733 events and 1461 unique users from 41 countries collected over 3 months. Prognosis was the most active category, with searches for Palliative Performance Scale accounting for a third of prognosis reads. Articles about dosage featured heavily, especially on methadone titration. On the spectrum of illness, physiological categories such as gastrointestinal and renal diseases were generally more popular than psychiatric disorders. Articles about interpersonal skills from categories such as Communication; Ethics, Law, Policy; and Psychosocial and Spiritual Experience were the least read. All of our conclusions are supported by chi-squared tests with P values <.01. More detailed results are included in the poster. Conclusions: This analysis shows that most users consult the app for guidance in symptom management and prognosis. The usage patterns described above suggest that the app is likely being used at the point of care as a clinical reference for medical decision making and therapeutic guidance. Our study provides evidence that mobile applications can be effective tools to distribute quality palliative care resources on a global scale. Our results also indicate which topics should be emphasized in medical education and how increased vigilance about these topics can optimize patient care; with a large active user base, we have the opportunity to make even more precise conclusions in the future.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».