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 patien...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13049-022-01020-6 |
| _version_ | 1811235466167451648 |
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| author | Mirjam Lisa Scholz Helle Collatz-Christensen Stig Nikolaj Fasmer Blomberg Simone Boebel Jeske Verhoeven Thomas Krafft |
| author_facet | Mirjam Lisa Scholz Helle Collatz-Christensen Stig Nikolaj Fasmer Blomberg Simone Boebel Jeske Verhoeven Thomas Krafft |
| author_sort | Mirjam Lisa Scholz |
| collection | DOAJ |
| description | 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 aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment. Methods Stroke patient data (n = 9049) from the years 2016–2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI. Results The Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted. Conclusions An ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through the 1813-Medical Helpline, and on weekends. Trial registration This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122). |
| first_indexed | 2024-04-12T11:52:21Z |
| format | Article |
| id | doaj.art-293419adf2c1406a8d7cb9b430c28b6f |
| institution | Directory Open Access Journal |
| issn | 1757-7241 |
| language | English |
| last_indexed | 2024-04-12T11:52:21Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine |
| spelling | doaj.art-293419adf2c1406a8d7cb9b430c28b6f2022-12-22T03:34:08ZengBMCScandinavian Journal of Trauma, Resuscitation and Emergency Medicine1757-72412022-05-0130111710.1186/s13049-022-01020-6Artificial 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 pointMirjam Lisa Scholz0Helle Collatz-Christensen1Stig Nikolaj Fasmer Blomberg2Simone Boebel3Jeske Verhoeven4Thomas Krafft5Emergency Medical Services, Capital Region of DenmarkEmergency Medical Services, Capital Region of DenmarkEmergency Medical Services, Capital Region of DenmarkEmergency Medical Services, Capital Region of DenmarkEmergency Medical Services, Capital Region of DenmarkDepartment of Health, Ethics and Society, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht UniversityAbstract 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 aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment. Methods Stroke patient data (n = 9049) from the years 2016–2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI. Results The Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted. Conclusions An ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through the 1813-Medical Helpline, and on weekends. Trial registration This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122).https://doi.org/10.1186/s13049-022-01020-6Artificial intelligenceEmergency Medical ServicesStroke detectionAutomated speech recognition |
| spellingShingle | Mirjam Lisa Scholz Helle Collatz-Christensen Stig Nikolaj Fasmer Blomberg Simone Boebel Jeske Verhoeven Thomas Krafft 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 Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine Artificial intelligence Emergency Medical Services Stroke detection Automated speech recognition |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Artificial intelligence Emergency Medical Services Stroke detection Automated speech recognition |
| url | https://doi.org/10.1186/s13049-022-01020-6 |
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