- UHRA Home
- Browsing by Author
Browsing by Author "Howland, Ian"
Now showing items 1-3 of 3
-
Epidemiology of prehospital emergency calls according to patient transport decision in a middle eastern emergency care environment: Retrospective cohort‐based
Farhat, Hassan; Alinier, Guillaume; El Aifa, Kawther; Makhlouf, Ahmed; Gangaram, Padarath; Howland, Ian; Jones, Andre; Abid, Cyrine; Khenissi, Mohamed Chaker; Howard, Ian; Khadhraoui, Moncef; Castle, Nicholas; Al Shaikh, Loua; Laughton, James; Gargouri, Imed (2024-04-23)Background and Aim: Though emergency medical services (EMS) respond to all types of emergency calls, they do not always result in the patient being transported to the hospital. This study aimed to explore the determinants ... -
Exploring factors influencing time from dispatch to unit availability according to the transport decision in the pre-hospital setting: an exploratory study
Farhat, Hassan; Makhlouf, Ahmed; Gangaram, Padarath; Aifa, Kawther El; Khenissi, Mohamed Chaker; Howland, Ian; Abid, Cyrine; Jones, Andre; Howard, Ian; Castle, Nicholas; Al Shaikh, Loua; Khadhraoui, Moncef; Gargouri, Imed; Laughton, James; Alinier, Guillaume (2024-12)Background: Efficient resource distribution is important. Despite extensive research on response timings within ambulance services, nuances of time from unit dispatch to becoming available still need to be explored. This ... -
Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques
Farhat, Hassan; Makhlouf, Ahmed; Gangaram, Padarath; El Aifa, Kawther; Howland, Ian; Babay Ep Rekik, Fatma; Abid, Cyrine; Khenissi, Mohamed Chaker; Castle, Nicholas; Al-Shaikh, Loua; Khadhraoui, Moncef; Gargouri, Imed; Laughton, James; Alinier, Guillaume (2024-05-03)Background: The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for ...