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LOCATION:Murray Hill West
DTSTART;TZID=America/New_York:20260323T113000
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UID:HFESHCS_2026 International Symposium on Human Factors and Ergonomics i
 n Health Care_sess114_LEC331@linklings.com
SUMMARY:No Handoff Left Behind: AI Support for Critical Care Transitions
DESCRIPTION:Zander N. Miler (Embry-Riddle Aeronautical University); Liam D
 . Brennan (Embry-Riddle Aeronautical University, Limb & Branch Businesses 
 LLC); and Elizabeth R. Merwin, Caleb G. Blackmon, and Joseph R. Keebler (E
 mbry-Riddle Aeronautical University)\n\nAs artificial intelligence (AI) co
 ntinues to be developed and tuned, the healthcare industry has been increa
 singly focused on utilizing AI technologies to improve patient care and th
 e efficiency of clinicians through decision-making support, patient data a
 nd diagnostics, and risk classification and prevention (Secinaro et al., 2
 021; Yu et al., 2018). More recently, researchers have proposed and examin
 ed the use of AI to assist in patient handoffs through means of generative
  reports (Landman et al., 2024; Genes et al., 2025) and clinical complicat
 ion risk identification and predictions (Abraham et al., 2023; Xue et al.,
  2021).\n\nA handoff is the transfer of care, responsibility, and liabilit
 y of a patient or group of patients from one set of care providers to anot
 her (Desmedt et al., 2021). Effective handoffs are essential for high-qual
 ity patient care (Keebler et al., 2016); however, their effectiveness is l
 imited by unstandardized handoff protocols, information omissions, inaccur
 ate information, skill deficits, communication breakdowns, and inadequate 
 training (Abraham et al., 2014). Despite recent research on AI use for inp
 atient-to-inpatient or emergency department (ED) to inpatient handoffs, th
 ere is currently a lack of literature examining AI in emergency medical se
 rvice (EMS) to ED or inter-facility patient handoffs. EMS to ED handoffs d
 eal with unique challenges, such as the variable and chaotic environment, 
 stress levels, and interprofessional interactions (Troyer & Brady, 2020). 
 Interfacility patient handoffs are also at greater risk of having poor pat
 ient outcomes due to patient status changes during transfer, poor transfer
  processes, lack of standardized tools between facilities, and interoperab
 ility issues (Galatzan et al., 2024). This is beyond the fact that transfe
 rs are typically done due to a complication or procedure that the current 
 facility is not equipped to handle.\n\nVarious approaches of AI can, in th
 eory, be utilized to support clinical handoffs in general; however, EMS an
 d interfacility handoffs contain unique situations and challenges that may
  make the use of various models more difficult. For example, the loud envi
 ronment in an ambulance would interfere with AI effectively performing tas
 ks that rely on efficient speech recognition. Three primary AI model types
  would be suited for clinical handoff support. First, natural language pro
 cessing (NLP) can analyze and understand human language in both spoken and
  written forms and then convert the data into a valuable and structured fo
 rmat (Zhou et al., 2022). Second, generative AI can synthesize information
  into a summative report that can be used during the handoff (Genes et al.
 , 2025). Lastly, predictive models can be used to diagnose health issues a
 nd predict survival rates (Rong et al., 2020) as well as identify and stra
 tify risk factors to aid in prevention for patients (Secinaro et al., 2021
 ; Yu et al., 2018).\n\nGaps in AI utilization in EMS and interfacility han
 doffs are evident in both practice and literature. While there has been pr
 omise in utilizing AI models for various purposes within healthcare and ev
 en in some handoff settings, as previously discussed, this has not been ex
 amined in an EMS or interfacility context. Given the unique challenges eac
 h of these contexts faces, it is paramount to begin examining these unique
  AI use cases.\n\nChallenges for the integration of AI systems should also
  be considered. Aung et al. (2021) detail several categories containing a 
 variety of implementation challenges, including social factors such as tru
 st in AI and misunderstanding, technology/development factors such as bias
  and data overfitting, data acquisition factors such as data availability 
 and quality, implementation factors such as stakeholder buy-in and the cur
 rent lack of evidence, and ethical factors such as privacy, safety, and ac
 countability.  While many of these factors have been explored in the AI li
 terature across various domains and contexts, it has been underexplored wi
 thin clinical handoffs, especially in the EMS to ED and interfacility cont
 exts.\n\nFuture research on AI-assisted handoffs in the discussed contexts
  should evaluate whether they improve patient outcomes, such as mortality 
 rates, length of stay, and the incidence of adverse outcomes, by having a 
 higher quality handoff defined by factors including, but not limited to, i
 nformation omission, inaccurate information, and handoff duration. AI inte
 gration challenges previously discussed should also be heavily researched.
  Although AI integration solutions in the literature would work for these 
 transitions in theory, they must be tested extensively and adapted based o
 n results. Furthermore, AI tools should be co-designed and tested with EMS
  personnel and frontline clinical staff to ensure a high-quality user expe
 rience (UX). Research should also be conducted to examine situational occu
 rrences in these high-risk transitions, such as interoperability issues be
 tween organizations. Lastly, research into developing AI technologies shou
 ld aim for any systems implemented to be that are capable of functioning o
 ffline, over extended durations, and in austere environments.  While this 
 goes beyond the day-to-day standards necessitated by many urban EMS provid
 ers, by ensuring a more robust system, this technology can be introduced i
 nto more rural and backcountry settings over time, in addition to being be
 tter equipped for mass-casualty, natural disaster, and other extreme urban
  scenarios where internet, radio, and cell phone signals may be impaired (
 Freeman et al., 2008).\n\nAI-assisted handoffs may facilitate significantl
 y higher quality handoffs for EMS and interfacility patient handoffs, redu
 cing poor patient outcomes in addition to decreasing the mental workload (
 MWL) and stress of frontline staff. On the contrary, AI systems can also c
 ause negative outcomes, such as biasing clinical providers and creating co
 mplacency for critical care decisions. Furthermore, if the handoffs in the
 se settings remain overlooked in the general and AI literature, these care
  settings risk falling further behind in the quality of care they provide.
  By examining the gaps and challenges of AI use in EMS and interfacility h
 andoffs, a high-risk sector, this work lays the groundwork for developing 
 solutions to enhance the quality of care and patient safety in these setti
 ngs.\n\nTrack: Hospital Environments\n\nSession Chair: Gabriel Segarra (Me
 dical University of South Carolina)\n\n
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