Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

Logo MUG-Forschungsportal

Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid Stoffw Microb

Jauk, S; Kramer, D; Avian, A; Berghold, A; Leodolter, W; Schulz, S.
Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study.
J MED SYST. 2021; 45(4): 48-48. Doi: 10.1007/s10916-021-01727-6 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Führende Autor*innen der Med Uni Graz
Jauk Stefanie
Co-Autor*innen der Med Uni Graz
Avian Alexander
Berghold Andrea
Schulz Stefan
Altmetrics:

Dimensions Citations:

Plum Analytics:

Scite (citation analytics):

Abstract:
Early identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the acceptance of the integration of such complex models in clinical routine remains unclear. The aim of this study was to evaluate user acceptance of an already implemented machine learning-based application predicting the risk of delirium for in-patients. We applied a mixed methods design to collect opinions and concerns from health care professionals including physicians and nurses who regularly used the application. The evaluation was framed by the Technology Acceptance Model assessing perceived ease of use, perceived usefulness, actual system use and output quality of the application. Questionnaire results from 47 nurses and physicians as well as qualitative results of four expert group meetings rated the overall usefulness of the delirium prediction positively. For healthcare professionals, the visualization and presented information was understandable, the application was easy to use and the additional information for delirium management was appreciated. The application did not increase their workload, but the actual system use was still low during the pilot study. Our study provides insights into the user acceptance of a machine learning-based application supporting delirium management in hospitals. In order to improve quality and safety in healthcare, computerized decision support should predict actionable events and be highly accepted by users.
Find related publications in this database (using NLM MeSH Indexing)
Algorithms -
Algorithms -
Clinical Decision-Making -
Delirium - diagnosis
Diagnosis, Differential -
Diagnostic Errors - statistics & numerical data
Electronic Health Records - standards
Female -
Humans -
Machine Learning - statistics & numerical data
Male -
Middle Aged -
Pilot Projects -
Psychiatric Status Rating Scales -

Find related publications in this database (Keywords)
Clinical decision support
Delirium
Machine learning
Predictive modelling
Risk management
Technology acceptance model
© Med Uni Graz Impressum