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"Samita M. Heslin"

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"Samita M. Heslin"

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AI & Digital Health

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Artificial intelligence as cognitive support in emergency medicine
Clin Exp Emerg Med. 2026;13(3):254-257.   Published online April 3, 2026
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Artificial intelligence as cognitive support in emergency medicine
Clin Exp Emerg Med. 2026;13(3):254-257.   Published online April 3, 2026
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Artificial intelligence in emergency medicine critical care
Clin Exp Emerg Med. 2026;13(1):1-4.   Published online March 31, 2026
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Artificial intelligence in emergency medicine critical care
Clin Exp Emerg Med. 2026;13(1):1-4.   Published online March 31, 2026
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  • Interpretable mortality prediction at VA-ECMO establishment in acute myocardial infarction: A multicenter study from the CSECLS registry
    Haitao Bian, Beilei Yuan, Peng Wu, Zhongman Zhang, Xufeng Chen, Xiaotong Hou, Huazhong Zhang
    International Journal of Medical Informatics.2026; 220: 106641.     CrossRef
  • 2,509 View
  • 41 Download
  • 1 Web of Science
  • 1 Crossref
Original Article

Emergency Medicine Practice and Administration

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A physician-nurse team adjacent to triage shortens length of stay and left-without-being-seen rates
Clin Exp Emerg Med. 2025;12(1):26-34.   Published online September 6, 2024
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A physician-nurse team adjacent to triage shortens length of stay and left-without-being-seen rates
Clin Exp Emerg Med. 2025;12(1):26-34.   Published online September 6, 2024
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Objective
Staffing significantly influences emergency department (ED) throughput; however, there is a shortage of registered nurses (RNs), impacting ED flow and crowding. Non-RN providers, like licensed practical nurses (LPNs), could potentially assist with tasks traditionally assigned to RNs. To improve the front-end ED process, we implemented an attending physician–LPN team (PNT) positioned next to triage and utilized existing ED hallway space. Methods This study took place at a tertiary care ED with over 110,000 annual visits. We compared postintervention (post-PNT) data (November 1, 2022–February 28, 2023) to preintervention (pre-PNT) data (July 31, 2022–October 31, 2022). The PNT, positioned adjacent to triage, expedited care for ED patients awaiting open rooms. The PNT selected patients from the waiting room to bypass the main ED, evaluated them in a private room, and then moved them to the hallway pending further care. Multivariable regression analysis was utilized to measure the impact of different factors on ED length of stay (LOS). Results We analyzed 23,516 patient visits, 10,288 in the pre-PNT period and 13,288 in the postPNT period. Post-PNT consisted of 2,454 PNT visits and 10,834 non-PNT visits. The intervention led to significant improvements, including a decrease in mean ED LOS from 492 to 425 minutes, a decrease in 72-hour revisits from 5.1% to 4.0%, a decrease in left-without-being-seen rate from 6.7% to 3.3%, and a decrease in the mean arrival-to-provider time from 74 to 60 minutes. Multivariable regression analysis showed that ED LOS was significantly lower for post-PNT patients than pre-PNT. Conclusion By leveraging the scope of LPNs and utilizing existing ED space, the PNT model successfully reduced front-end bottlenecks, leading to improved throughput and reduced revisitation and left-without-being-seen rates.

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  • Operational Challenges and Management Strategies Affecting Emergency Department Length of Stay: A Literature Review
    Vindy Vanessa Wennas
    Indonesian Journal of Innovation Multidisipliner Research.2026; 4(2): 5167.     CrossRef
  • 13,636 View
  • 261 Download
  • 1 Web of Science
  • 1 Crossref