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Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques.

Hassan FarhatAhmed MakhloufPadarath GangaramKawther El AifaIan HowlandFatma Babay Ep RekikCyrine AbidMohamed Chaker KhenissiNicholas CastleLoua Al-ShaikhMoncef KhadhraouiImed GargouriJames LaughtonGuillaume Alinier
Published in: PloS one (2024)
The study identified the transformative potential of ML algorithms in enhancing the quality of pre-hospital care in Qatar. The high predictive accuracy of the employed models suggested actionable avenues for day and time-specific resource planning and patient triaging, thereby having potential to contribute to pre-hospital quality, safety, and value improvement. These findings pave the way for more nuanced, data-driven quality improvement interventions with significant implications for future operational strategies.
Keyphrases
  • quality improvement
  • healthcare
  • acute care
  • patient safety
  • machine learning
  • palliative care
  • deep learning
  • current status
  • pain management
  • chronic pain
  • risk assessment