Saudi Cultural Missions Theses & Dissertations

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    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, Sue
    Aim: 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.
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    A Mixed-Methods Study to Investigate the Awareness by Pilgrims and the Saudi Authorities of Health Risks Arising From the Hajj Pilgrimage in Saudi Arabia
    (Saudi Digital Library, 2023-09-28) Almehmadi, Mater; David, Alexander
    One of the most important factors in developing preventative measures is awareness of health risks among public authorities and the public themselves. The coronavirus pandemic of 2020-2023 has exposed significant weaknesses in public health systems that need to be addressed, although research has so far been limited with respect to studies that have explored the perceptions of both the public and authorities about the uptake of preventative health measures. As it is the host of the annual Hajj pilgrimage, Saudi Arabia offers a good case study of the health management of one of the biggest mass gatherings in the world. Although the health strategy here usually involves an array of preventative measures, the uptake among pilgrims is extremely low. As a case study exploring the factors that determine uptake, the Hajj pilgrimage is approached in this dissertation using a mixed methodology for the collection of data from the officials of the Hajj and the individuals who participate in it. Some 280 participants were canvassed in the quantitative study. The findings are that 94% considered the Hajj to be safe and limited themselves to taking pre-travel advice on health, while 70% of the respondents reported the diversity of the pilgrims to be the main factor threatening health outcomes. Overall, the study reported a significant shortfall in pilgrims’ perception of the health risks associated with the Hajj pilgrimage. Qualitative research was then utilised to collect data from 17 Hajj officials, using semi-structured interviews followed by thematic analysis. The key themes that emerged in the analysis include, first, the safety of the Hajj as perceived by Saudi officials; secondly, in the face of health risks, how the safety of pilgrims is maintained by Saudi officials; thirdly, avoiding the health risks of the Hajj pilgrimage; fourthly holding training sessions for the Hajj workers; fifthly, the pilgrims’ awareness of health risks; sixthly, the education of pilgrims about health risk in their individual countries; and finally, the use of new technologies to raise the pilgrims’ awareness about health instructions and measure their satisfaction regarding the outcomes.
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