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Clinical Decision Support for Extreme Prehospital Emergencies

Andrew W. Kirkpatrick*, Kyle Couperus, John Van Eaton, Jason R. Bingham, Jessica L. McKee, Christopher J. Colombo, Juan Wachs

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Critical medical emergencies are a constant threat to human wellness and are frequently caused by unexpected acute illness or traumatic injuries. They require urgent therapies to be delivered in the prehospital environment, where such care must be balanced against the competing need to transfer to definitive care. The clinical providers tasked with delivering such care are often inexperienced and must operate with incomplete medical facts, based on urgent assessments and significant time constraints. Thus, this extremely stressful environment always challenges and frequently overwhelms human cognitive capacity. As such, not only is inadequate care delivered, but the first responders tasked with these responsibilities will be frequent secondary victims due to the stresses involved with such high acuity care. Clinical decision support systems (CDSS) have generally been defined as interactive computing systems for situational decision-making that can improve decision efficiency and safety of care. However, the TeleMentored Ultrasound Supported Medical Interventions (TMUSMI) Research Group promotes the reality that the CDSS comprises a much broader operational paradigm that recognizes that experienced and knowledgeable caregivers can and should be remotely interfaced to distant less/inexperienced caregivers using modern informatics to guide and improve patient care and outcomes. It is important for all to understand that a CDSS may be delivered through numerous logistical paradigms that involve the following: machine learning only, telementored or videorecorded humans, or assisted reality scenarios involving the hybrid fusion of humans and machines. This practically means that the concept of augmented intelligence in prehospital care may have many practical configurations, with the best model still to be elucidated and validated.

Original languageEnglish
Title of host publicationHot Topics in Acute Care Surgery and Trauma
PublisherSpringer Nature
Pages91-106
Number of pages16
DOIs
StatePublished - 2026

Publication series

NameHot Topics in Acute Care Surgery and Trauma
VolumePart F1370
ISSN (Print)2520-8284
ISSN (Electronic)2520-8292

Keywords

  • Artificial intelligence
  • Augmented intelligence
  • Clinical decision support
  • Decision support
  • Remote telementoring
  • Resuscitation
  • Video modeling

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