Saudi Cultural Missions Theses & Dissertations
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Item Restricted Investigation into the clinical use of the non-invasive estimation of haemodynamic performance in timecritical patients in Emergency Departments.(University of New South Wales, 2025) Alharbi, Abdullah; Middleton, Paul; Leung, DominicIntroduction: Emergency departments (EDs) serve as the frontline of acute medical care, where rapid and accurate assessment of patients is critical for determining the appropriate level of intervention. Vital signs recorded at triage are essential for assessing a patient's immediate health status and helping clinicians prioritise care. However, traditional vital signs alone may not always provide a comprehensive picture of a patient's cardiovascular function. Recent advances in non-invasive cardiovascular monitoring, specifically Ultrasonic Cardiac Output Monitoring (USCOM), offer new possibilities for enhancing patient assessment at triage. USCOM provides detailed haemodynamic parameters, such as cardiac power (CP), which could potentially improve the accuracy of outcome predictions in ED patients. This study aimed to evaluate the clinical and predictive value of traditional vital signs and USCOM-derived cardiovascular parameters recorded at triage in ED patients. It compared conventional haemodynamic assessments with USCOM data to determine their association with patient outcomes. Methods: Two studies were conducted at Liverpool Hospital ED, Sydney. Study 1 was retrospective (May 2018–May 2021), analysing conventional vital signs in adult patients with triage categories 2 and 3. Study 2 was prospective (March 2023–October 2023), focusing on USCOM-derived parameters in similar patient groups. Results: Study 1 included 116,460 patients and identified several key predictive vital signs for mortality: hypoxia (OR 2.13), abnormal respiratory rate (OR 2.43), bradycardia/tachycardia (OR 1.55), and hypotension (OR 2.60). Age was the strongest predictor, with higher mortality and ICU admission in patients aged 40-65 and over 65. For the second study, we included 409 patients. CP was a strong predictor for ICU (OR 0.28) and hospital admissions (OR 0.11). A decline in cardiovascular function over the first six hours was associated with higher mortality. Conclusion: This study highlights the importance of triage vital signs in predicting patient outcomes and the potential of USCOM-derived cardiac power to improve risk stratification. Age-specific triage protocols are recommended due to the diminishing predictive value of certain vital signs with age. Future research should validate these findings and explore integrating advanced haemodynamic monitoring into routine practice.23 0Item Restricted The use of Artificial Intelligence in Emergency Triage versus Conventional Triage in Adult Patients Affects Emergency Department Overcrowding.(Univrsity of sydney, 2024-07-17) Alghamdi, Norah; Randall, SueAim: This review aims to identify whether using artificial intelligence (AI) in emergency triage versus conventional triage in adult patients affects emergency department (ED) overcrowding. Background: In emergency medicine, triage is an essential procedure for establishing patient care priorities according to severity (Defilippo et al., 2023). Typically, this practice depends on the experience and decision-making skills of emergency nurses, which can sometimes result in differences in how patients are evaluated and interruptions in receiving timely care. Due to several factors, such as ED overcrowding, the triage process can be delayed or misjudged. Overcrowding occurs when the number of patients waiting to be seen, examined, or discharged from the ED exceeds the ED's structural or personnel capacity (Cameron et al., 2009). Since the issue of human resources is a complex issue, finding alternative methods to aid the workforce such as Artificial Intelligence (AI) should be investigated. This paper aims to discover how using AI in emergency triage can reduce and accelerate the triage process. Methods: an integrative literature review was conducted via systematic research in three electronic databases, CINHAL, MEDLINE, and PubMed. The literature included studies that used artificial intelligence in triage and how AI affects emergency overcrowding. Some of these studies did not measure overcrowding directly but studied the effects of overcrowding, and how can reflect the care provided, and the time to initiate treatment. Results: AI-based triage systems, which use machine learning algorithms to forecast patient acuity, hospitalisation, and death, provide notable improvements over conventional methods. These systems reduce errors related to human judgment and cognitive biases by improving accuracy and efficiency in triage choices using clinical data and electronic health records (Lee et al., 2024; Shahbandegan et al., 2022). Conventional triage techniques, on the other hand, mostly rely on the interpretation of individual clinicians, leaving room for errors. AI-based solutions, such as those that employ extreme gradient boosting algorithms, offer real-time decision support, enhancing patient outcomes by recommending crucial treatments for prompt and suitable care in emergency rooms. When compared to conventional techniques, AI-based triage systems demonstrate higher prediction ability and the potential to improve emergency care. Conclusion: Artificial intelligence showed positive results in emergency health settings and reduced overcrowding. In addition, future research requires algorithm refinement to increase generalisability and mitigate false positive cases.55 0