Introduction: No existing mass casualty triage system has been scientifically scrutinized or validated. A recent work group sponsored by the Centers for Disease Control and Prevention, using a combination of expert opinion and the extremely limited research data available, created the SALT (sort-assess-lifesaving interventions-treat/transport) triage system to serve as a national model. An airport crash drill was used to pilot test the SALT system.
Objective: To assess the accuracy and speed with which trained paramedics can triage victims using this new system.
Methods: Investigators created 50 patient scenarios with a wide range of injuries and severities, and two additional uninjured victims were added at the time of the drill. Students wearing moulage and coached on how to portray their injuries served as "victims." Assuming proper application of the SALT system, the patient scenarios were designed such that 16 patients would be triaged as T1/red/immediate, 12 as T2/yellow/delayed, 14 as T3/green/minimal, and 10 as T4/black/dead. Paramedics were trained to proficiency in the SALT system one week prior to the drill using a 90-minute didactic/practical session, and were given "flash cards" showing the triage algorithm to be used if needed during the drill. Observers blinded to the study purpose timed and recorded the triage process for each patient during the drill. Simple descriptive statistics were used to analyze the data.
Results: The two paramedics assigned to the role of triage officers applied the SALT algorithm correctly to 41 of the 52 patients (78.8% accuracy). Seven patients intended to be T2 were triaged as T1, and two patients intended to be T3 were triaged as T2, for an overtriage rate of 13.5%. Two patients intended to be T2 were triaged as T3, for an undertriage rate of 3.8%. Triage times were recorded by the observers for 42 of the 52 patients, with a mean of 15 seconds per patient (range 5-57 seconds).
Conclusions: The SALT mass casualty triage system can be applied quickly in the field and appears to be safe, as measured by a low undertriage rate. There was, however, significant overtriage. Further refinement is needed, and effect on patient outcomes needs to be evaluated.
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http://dx.doi.org/10.1080/10903120802706252 | DOI Listing |
PLOS Digit Health
January 2025
Clinical Care & Research, ORTEC B.V., Zoetermeer, The Netherlands.
Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level.
View Article and Find Full Text PDFBackground: The widespread availability of effective disease-modifying therapies would be a breakthrough in slowing the progression of early Alzheimer's disease (AD) to later stages of dementia. The purpose of this study is to explore how primary care capacity, variation in patient care-seeking, and geographic variation in capacity can affect the delivery of AD-modifying therapies.
Method: We used a county-level simulation model to assess patient demand and provider supply for the delivery of AD-modifying therapies.
Sci Rep
January 2025
Department of Biomedical Engineering, Kyung Hee University, 446-701 Electronic Information College Building, Kyunghee Univ, Global Campus, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi, Republic of Korea.
Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI model using ICD-10 (International Classification of Diseases, Tenth Revision) codes with other clinical variables to predict in-hospital mortality among trauma patients from a nationwide database. This study aimed to externally validate the performance of the AI model.
View Article and Find Full Text PDFAnn Am Thorac Soc
January 2025
The University of Utah School of Medicine, Division of Epidemiology, Department of Internal Medicine, Salt Lake City, Utah, United States.
Rationale: Patients with sepsis and/or acute respiratory failure are at high risk for death or long hospital stays, yet limited evidence exists to guide triage to intensive care units (ICUs) or general medical wards for the majority of these patients who do not initially require life support.
Objectives: To identify factors that influence how hospitals triage patients with capacity-sensitive conditions and those factors that may account for observed ICU relative to ward, or ward relative to ICU, benefits for such patients.
Methods: We conducted an explanatory sequential mixed-methods study.
Sensors (Basel)
December 2024
Organ Support and Automation Technologies Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA.
Prehospital medical care is a major challenge for both civilian and military situations as resources are limited, yet critical triage and treatment decisions must be rapidly made. Prehospital medicine is further complicated during mass casualty situations or remote applications that require more extensive medical treatments to be monitored. It is anticipated on the future battlefield where air superiority will be contested that prolonged field care will extend to as much 72 h in a prehospital environment.
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