Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework
2
Zitationen
5
Autoren
2023
Jahr
Abstract
The implementation process in the routine clinical care of a new predictive tool based on machine learning algorithms has been investigated using the RE-AIM framework. Semi-structured qualitative interviews have been conducted with a broad range of clinicians to elucidate potential barriers and facilitators of the implementation process across five major domains: Reach, Efficacy, Adoption, Implementation, and Maintenance. The analysis of 23 clinician interviews demonstrated a limited reach and adoption of the new tool and identified areas for improvement in implementation and maintenance. Future implementation efforts of machine learning tools should support the proactive engagement of a wide range of clinical users since the very initiation of the predictive analytics project, provide higher transparency of the underlying algorithms, employ broader onboarding of all potential users on a periodic basis, and collect feedback from clinicians on an ongoing basis.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.336 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.207 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.607 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.476 Zit.