https://journal.achsm.org.au/index.php/achsm <p>The Asia Pacific Journal of Health Management (APJHM) is a peer-reviewed journal for managers of organisations offering healthcare and aged care services. The APJHM aims to promote the discipline of health management throughout the region by facilitating the transfer of knowledge among readers by widening the evidence base for management practices.<br /><br />*Print 1(1);2006 - 5(1);2010 Online 4(2);2009 - current<br />*ISSN 2204-3136 (online); ISSN 1833-3818 (print)</p> Australasian College of Health Service Management en-US Asia Pacific Journal of Health Management 1833-3818 https://journal.achsm.org.au/index.php/achsm/article/view/6423 <p><strong>Background:</strong> The integration of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) in nursing management and healthcare delivery is rapidly advancing to address challenges in patient safety and operational efficiency. However, a comprehensive synthesis regarding the clinical, operational, and humanistic impacts of these technologies remains essential.</p> <p><strong>Objective:</strong> This study aims to synthesize empirical evidence on the roles of AI, ML, and IoT in enhancing patient safety, optimizing workforce efficiency, and mitigating cognitive overload among nursing personnel.</p> <p><strong>Methods:</strong> An integrative literature synthesis approach was conducted, analyzing 20 selected high-impact studies (n=20) published between 2020 and 2026 to evaluate and map scientific evidence regarding the application of AI/ML and IoT in clinical practice and nursing management.</p> <p><strong>Results:</strong> The integrative synthesis reveals four core findings: (1) EMR-integrated predictive algorithms enable early sepsis prediction up to 6 hours faster and detect high-risk fall movements within 30 seconds, reducing in-hospital mortality; (2) Intelligent signal filtering reduces false alerts by up to 92.25% and unnecessary notifications by 99.3%, directly mitigating alarm fatigue and medical errors; (3) Deploying automated AI scheduling and virtual "AI Sitters" yields measurable operational cost savings, enhances schedule transparency, and improves nurse job satisfaction; and (4) Embedding human-centered design, "empathy AI", and digital literacy frameworks (e.g., the N.U.R.S.E.S framework) provides the necessary infrastructure to overcome algorithmic bias and workflow disruptions.</p> <p><strong>Conclusion:</strong> AI, ML, and IoT serve as vital decision-support tools designed to augment rather than replace human nursing oversight. When implemented through a human-centered approach, these technologies serve as a cognitive shield, liberating nurses' bandwidth to preserve essential empathetic care while elevating patient safety and operational efficiency.</p> <p>&nbsp;</p> Muhammad basirun basirun Copyright (c) 21 1 https://journal.achsm.org.au/index.php/achsm/article/view/6422 <p><strong>Objective- </strong>To identify and assess the barriers and challenges faced by the pregnant women in the urban slum areas of Meerut city, Uttar Pradesh, India, in accessing the maternity care and interpret them within the context of Birth Preparedness and Complication Readiness (BPCR).</p> <p><strong>Study Design- </strong>A community based, descriptive cross sectional study was conducted using a structured questionnaire which was interviewer administered on a five-point Likert scale (1= strongly disagree to 5= strongly agree).</p> <p><strong>Study Setting and Participants- </strong>The study participants were 110 pregnant women aged 18-35 years residing in six urban slum areas of Meerut City.</p> <p><strong>Results- </strong>The barriers were pervasive and they were clustered around the three delays. Geographic/ transport - 92.7% pregnant women reported that transport availability at night time was difficult, 90% cited that the condition of the roads was poor, 74.5% perceived that the facilities were too far off and 76.4% reported that the slum location is difficult to be accessed. Finance -94.5% pregnant women found private hospital care unaffordable, 55.5% reported delay for want of money, and only 15.5% had made any financial preparations, Facility/ quality of care -99.1% reported unclean facilities, 98.2% reported poor postpartum care, 90% reported rude staff behavior, 86.4% reported overcrowding, 80.8% reported inattentive staff, 85.5% said that these experiences discourage public facility use and 80% feared mistreatment. Sociocultural - 67.3% said that their in-laws were dominant decision makers for maternity care.</p> <p><strong>Conclusion- </strong>Accessibility failure in these urban slum areas is driven less by distance then by cost, poor perceived quality of care, constrained women’s autonomy, unreliable emergency transport. Policymakers and Healthcare workers should focus on family inclusive ASHA- led BPCR counseling with tangible improvements in facility cleanliness, quality of care, respectful care, and night transport.</p> Dr Mohd Faisal Khan Ms. Aliya Anwar Thakur Dr. P.S. Raychaudhuri Copyright (c) 21 1