The Journal of Medicine, Law & Public Health <p><em>The</em> Journal <em>of</em> Medicine, Law <em>&amp;</em> Public Health (JMLPH) is an interdisciplinary publication that explores the intersection of medical practice, legal considerations, and public health policy. It aims to serve as a platform for professionals and academics from various fields to discuss and disseminate research findings, legal analysis, and policy discussions that impact health outcomes and healthcare delivery. The journal publishes a range of content, including original research, review articles, case studies, and commentaries, all of which undergo a rigorous peer-review process to ensure high-quality and relevant contributions to the literature. JMLPH is designed for a diverse readership, including healthcare providers, legal experts, public health practitioners, researchers, and policymakers. Through its publications, JMLPH seeks to inform and influence practice and policy, promote multidisciplinary collaboration, and encourage the integration of health, law, and public health principles in addressing contemporary health issues</p> JMLPH en-US The Journal of Medicine, Law & Public Health 2788-9815 A critical case commentary on Avon and Wiltshire Mental Health Partnership and North Bristol NHS Trust v WA and DT: Did the judge reach a fair and just decision regarding the capacity and best interest of the person? <p><em>Avon and Wiltshire Mental Health Partnership and North Bristol NHS Trust v WA and DT </em><u>[</u>2020] EWCOP<em>,</em> [2020] 7 WLUK 271: This case critically evaluates the findings on <em>WA</em>’s capacity assessment and best interest decision made by the courts to determine the treatment plan. From the finding’s on <em>WA</em>’s capacity, it is apparent that the way capacity testing was dealt with was not in accordance with the MCA guiding principles, but rather based on a subjective view of the assessors. Consequently, <em>WA </em>was held to be incapacitated requiring the courts to look at the best interest provisions under section 4 of the MCA in discussing the proposed treatment options. The positive aspect to this case was that Hayden J gave greater weight to <em>WA</em>’s wishes and feelings, and affirmed his autonomy.</p> Lisa Kachina Poku Copyright (c) 2024 Lisa Kachina Poku 2024-01-12 2024-01-12 4 1 317 326 10.52609/jmlph.v4i1.110 Avoiding Medication Errors Caused by Nurses in the Emergency Department in Saudi Arabia <p><strong>Background:</strong> Medication errors are pervasive in healthcare, especially in emergency rooms, with diverse causes that warrant critical investigation due to the potential repercussions for both patients and healthcare providers.</p> <p><strong>Aim: </strong> This research explores nurses' perspectives on medication errors in the emergency department.</p> <p><strong>Methods: </strong>A descriptive cross-sectional design involved 96 nurses, using a questionnaire that covered demographic data and nurses' perceptions of error causes, reporting practices, and barriers.</p> <p><strong>Results: </strong>The average age of the nurses who participated in this study was 27.7 ± 3.4 years, with 7.3 ± 1.9 years of experience. Most nurses (87.2%) were women. The majority held bachelor's degrees (88.3%) and worked fixed shifts (54.2%), and 46.8% reported medication errors in the past year, primarily occurring once (69.04%). They reported no complications in 97.5% of errors.</p> <p><strong>Conclusion:</strong></p> <p>Common error types included infusion rate errors, double dosing, and medication omission. Although errors are widespread, adverse consequences are infrequent, mainly occurring during prescribing or administration stages. Encouraging disclosure by nurses and fostering positive responses from hospital management are crucial for enhancing patient safety. Awareness of recovery mechanisms informs potential interventions to minimise overall safety. </p> <p><strong>Key words: </strong>Medication Errors, Emergency Department, Nurses, Understanding, Perception</p> <p> </p> <p> </p> Yasir Ahmed Alkhuzama Hasson Alhasson Waladin Faiz Mahrus Talal Marui Asiri Sara ahmed Alsuwayed Alaa Turki Alturki Moneerah Mohammed Alzoman Kassem Jawad Alobaid Copyright (c) 2023 Yasir Ahmed, Alkhuzama Hasson Alhasson, Waladin Faiz Mahrus, Talal Marui Asiri, Sara ahmed Alsuwayed, Alaa Turki Alturki, Moneerah Mohammed Alzoman, Kassem Jawad Alobaid 2023-12-25 2023-12-25 4 1 307 311 10.52609/jmlph.v4i1.101 Evaluating the Precision of ChatGPT Artificial Intelligence in Emergency Differential Diagnosis <p><strong>Introduction:</strong> artificial intelligence (AI) is the study and development of intelligent machines that can carry out tasks that would typically require human intelligence. AI seeks to give machines the ability to think, problem-solve, sense their surroundings, and comprehend human speech. By enhancing and optimising processes, this technology is predicted to completely transform a number of industries. Artificial intelligence is tipped to be the next technological breakthrough that will shape our future.</p> <p><br /><strong>Objective:</strong> This study focused on evaluating the precision of ChatGPT artificial intelligence in emergency differential diagnosis.</p> <p><br /><strong>Methods:</strong> This was a comparison study, conducted from August to September 2023, evaluating the ability of both the Monica ChatGPT and the emergency medicine textbooks to provide differential diagnoses for frequently occurring complaints. Twelve symptoms common to adult patients were included in the list of chief complaints. To gauge the accuracy of the ChatGPT’s answers, the researcher employed ChatGPT®-4 queries.</p> <p><br /><strong>Results:</strong> The total number of differential diagnoses captured by the two resources was 431. The ChatGPT captured a total of 272 differential diagnoses; however, 59 of these were not included in the list of the chief complaints.</p> <p><br /><strong>Conclusion:</strong> The study concludes that AI can be helpful in some situations, such as primary care diagnosis and patient triage, although in most cases it is not a better diagnostic tool. Therefore, AI and human diagnosis can be used concurrently in the health sector. </p> Abdullah Altamimi Abdullah Aldughaim Shahad Alotaibi Jumana Alrehaili Mohamad Bakir Ahmad Almuhainy Copyright (c) 2024 Abdullah Altamimi, Abdullah Aldughaim , Shahad Alotaibi, Jumana Alrehaili, Ahmad Almuhainy, Sharafaldeen Bin Nafisah 2024-03-02 2024-03-02 4 1 327 337 10.52609/jmlph.v4i1.113 Acute Myocardial Infarction Complicated by Death in a Young Medically Free Female: A Case Report <p style="font-weight: 400;">Cardiovascular disease, and particularly myocardial infarction (MI), is the leading cause of disability and death in women worldwide. Young females with MI have previously been disregarded from inclusion in mass epidemiological research and clinical trials due to the physiologically protective properties of oestrogen. Moreover, young women who present with chest pain are often not considered at a risk for MI, which often delays workup and timely intervention, and they remain an unrecognised and under-treated subgroup. We report a case of a 22-year-old, medically free female with MI, with no traditional risk factors, complicated by a final, unfortunate outcome of death. This case report highlights the need to thoroughly investigate the prevalence, symptoms, and types of acute MI in young female patients, as well to create validated tools to assess the risk factors associated with this subgroup.</p> Falwah Alharthi Abeer Alfadhliah Rawan Alosaimi Ahmed Alharkan Copyright (c) 2023 Falwah Alharthi, Abeer Alfadhliah, Rawan Alosaimi , Ahmed Alharkan 2023-12-26 2023-12-26 4 1 312 316 10.52609/jmlph.v4i1.107 Application of Artificial Intelligence in Paramedic Education: Current Scenario and Future Perspective: A Narrative Review <p style="font-weight: 400;"><strong>Background: </strong></p> <p style="font-weight: 400;">Artificial intelligence (AI) has the potential to revolutionise paramedic education. As well as allowing for personalised learning experiences tailored to individual needs and learning styles, it can provide simulations, intelligent tutoring systems, automated grading and assessment, and predictive analytics.</p> <p style="font-weight: 400;"><strong>Objective: </strong></p> <p style="font-weight: 400;">To investigate the role of artificial intelligence in transforming the landscape of paramedic education and evaluate its potential to improve learning outcomes.</p> <p style="font-weight: 400;"><strong>Methods: </strong></p> <p style="font-weight: 400;">This review presented the role of AI in paramedic education and its perspective over the past twenty years. It included high-quality data and comprehensive investigations of articles available in renowned databases.</p> <p style="font-weight: 400;"><strong>Results: </strong></p> <p style="font-weight: 400;">AI-based training and simulation technologies, such as virtual patients, surgical simulators, and intelligent tutoring systems,are increasingly being used in paramedic education. Virtual patients use computer-generated avatars to display symptoms and react to therapies, while surgical simulators use accurate anatomical models and haptic feedback devices to simulate surgical operations.</p> <p style="font-weight: 400;"><strong>Conclusion: </strong></p> <p style="font-weight: 400;">AI has the potential to fundamentally alter how students learn, the kind of education they receive, and the efficiency with which healthcare is delivered. It can create immersive training environments, analyse medical data, and help students feel more competent, confident, and capable. This potential can be harnessed to enhance paramedic education and improve patient care outcomes.</p> Meshal Menahi Alshebani Mohammed Qismi Alanazi Abdulrahman Eidhah Alanazi Mohammed Abdulrahman Almotlaq Faisal Mabkhoot ALdawsari Abdulaziz Mohammed Almeshari Abdullah R. Nofal Copyright (c) 2023 Meshal Menahi Alshebani, Mohammed Qismi Alanazi, Abdulrahman Eidhah Alanazi, Mohammed Abdulrahman Almotlaq, Faisal Mabkhoot ALdawsari, Abdulaziz Mohammed Almeshari, Abdullah R. Nofal 2023-12-25 2023-12-25 4 1 299 306 10.52609/jmlph.v4i1.98