Login
- Titel:
Identifying potentials for Artificial Intelligence-based process support along the emergency department care pathway to alleviate overcrowding
- Dokumenttyp:
- Konferenzbeitrag
- Autor(en):
- Born, Cornelius; Wildmoser, Julian; Schwarz, Romy; Böttcher, Timo; Hein, Andreas; Krcmar, Helmut
- Abstract:
- This study examines the challenge of overcrowding within Emergency Department (ED) processes and elucidates the potential for AI-based interventions to enhance patient care and operational efficiency. We used qualitative interviews conducted in two German hospitals to identify five challenges along the ED care pathway: limited demand predictability, task overload, information unavailability, lack of IT systems interoperability, and low process standardization. We propose AI-based solutions to target these issues. For example, demand forecasting models could optimize resource allocation during patient arrival, while AI-guided queries and Decision Support Systems could improve data quality and standardization in registration & triage and diagnosis & treatment, respectively. Additionally, AI-driven text recognition and monitoring could streamline information management and patient observation. While the study is constrained by its geographic and methodological scope, it is foundational work for future research, including Design Science Research approaches to validate and implement the proposed AI-based process aids in the ED.
- Stichworte:
- Emergency Department; Overcrowding; Crowding; Artificial Intelligence; Machine Learning
- Kongress- / Buchtitel:
- International Conference on Health and Social Care Information Systems and Technologies
- Jahr:
- 2024
- Quartal:
- 4. Quartal
- Reviewed:
- ja
- Sprache:
- en
- Volltext / DOI:
- doi:https://doi.org/10.1016/j.procs.2024.06.348
- Semester:
- WS 24-25
BibTeX