Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The Paradoxes of Digital Tools in Hospitals: Qualitative Interview Study (Preprint)
2
Zitationen
3
Autoren
2024
Jahr
Abstract
<sec> <title>BACKGROUND</title> Digital tools are progressively reshaping the daily work of health care professionals (HCPs) in hospitals. While this transformation holds substantial promise, it leads to frustrating experiences, raising concerns about negative impacts on clinicians’ well-being. </sec> <sec> <title>OBJECTIVE</title> The goal of this study was to comprehensively explore the lived experiences of HCPs navigating digital tools throughout their daily routines. </sec> <sec> <title>METHODS</title> Qualitative in-depth interviews with 52 HCPs representing 24 medical specialties across 14 hospitals in Switzerland were performed. </sec> <sec> <title>RESULTS</title> Inductive thematic analysis revealed 4 main themes: digital tool use, workflow and processes, HCPs’ experience of care delivery, and digital transformation and management of change. Within these themes, 6 intriguing paradoxes emerged, and we hypothesized that these paradoxes might partly explain the persistence of the challenges facing hospital digitalization: the promise of efficiency and the reality of inefficiency, the shift from face to face to interface, juggling frustration and dedication, the illusion of information access and trust, the complexity and intersection of workflows and care paths, and the opportunities and challenges of shadow IT. </sec> <sec> <title>CONCLUSIONS</title> Our study highlights the central importance of acknowledging and considering the experiences of HCPs to support the transformation of health care technology and to avoid or mitigate any potential negative experiences that might arise from digitalization. The viewpoints of HCPs add relevant insights into long-standing informatics problems in health care and may suggest new strategies to follow when tackling future challenges. </sec>
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.214 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.071 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.429 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 · 5.418 Zit.