2022
DOI: 10.1186/s13049-022-01020-6
|View full text |Cite
|
Sign up to set email alerts
|

Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point

Abstract: Background and purpose Stroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arrive at the hospital within the time-to-treatment. An automatic speech recognition software (ASR) can increase the recognition of Out-of-Hospital cardiac arrest (OHCA) at the EMS by 16%. This research ai… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
9
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 16 publications
(9 citation statements)
references
References 76 publications
0
9
0
Order By: Relevance
“…Automated voice recognition software [ 32 ] and data analysis [ 33 ] have proven valuable in recognizing pathologies such as stroke and cancer, facilitating early detection and avoiding a delay in treatment. CDS systems [ 31 , 36 ] or intelligent checklists [ 59 ] can avoid harm to patients deriving from inadequate treatment or non-conformity to guidelines and good clinical practices.…”
Section: Resultsmentioning
confidence: 99%
“…Automated voice recognition software [ 32 ] and data analysis [ 33 ] have proven valuable in recognizing pathologies such as stroke and cancer, facilitating early detection and avoiding a delay in treatment. CDS systems [ 31 , 36 ] or intelligent checklists [ 59 ] can avoid harm to patients deriving from inadequate treatment or non-conformity to guidelines and good clinical practices.…”
Section: Resultsmentioning
confidence: 99%
“…Consequently, there is a growing body of research utilizing artificial intelligence (AI) algorithms based on deep learning to enhance the quality of pre-hospital emergency care. Many studies have confirmed that AI in emergency medicine improves accuracy and efficiency, and reduces time-to-treatment for the detection of out-of-hospital cardiac arrests (OHCA), stroke detection [ 91 ], and EKG interpretation for ST-elevation myocardial infarction (STEMI) [ 92 ]. Unfortunately, most of the data obtained from the aforementioned scenarios may not have been adequately protected.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, artificial intelligence using automatic speech recognition and noise evaluation might be applicable for this indication. Such tools have already been developed in Emergency Medical Call Centre for cardiac arrest or stroke detection [19,20].…”
Section: Discussionmentioning
confidence: 99%