AI-enhanced healthcare management during natural disasters: conceptual insights

Engineering Science & Technology Journal, Год журнала: 2024, Номер 5(5), С. 1794 - 1816

Опубликована: Май 21, 2024

Natural disasters often lead to significant disruptions in healthcare delivery, exacerbating the already formidable challenges faced by systems. Leveraging artificial intelligence (AI) offers a promising approach mitigate these and enhance management during after natural disasters. This conceptual paper aims propose framework for integration of AI into disaster response efforts, with focus on optimizing resource allocation, improving patient triage, enhancing overall system resilience. Through comprehensive review existing literature, this identifies gaps current practices explores potential address shortcomings. By analyzing case studies examples from previous disasters, highlights transformative impact that technologies such as predictive analytics, machine learning, robotics can have delivery crisis situations. The objectives are twofold: define strategic incorporating protocols outline expected outcomes implementing framework. Expected benefits include expedited triage processes, more accurate improved communication systems, ultimately leading better enhanced efficiency. proposed emphasizes importance interdisciplinary collaboration between professionals, technologists, policymakers, experts. It also addresses ethical considerations associated implementation settings. In conclusion, underscores critical role bolstering capabilities leveraging technologies, systems become adaptive, responsive, resilient face unforeseen challenges, saving lives minimizing communities. Keywords: AI-Enhanced Healthcare Management, Disasters, Conceptual Insights.

Язык: Английский

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Navigating Healthcare Complexity

Advances in business strategy and competitive advantage book series, Год журнала: 2024, Номер unknown, С. 145 - 168

Опубликована: Сен. 13, 2024

The chapter discusses the importance of integrating business principles into nursing leadership to improve healthcare delivery. It highlights need for nurse leaders be knowledgeable in strategic planning, financial management, human resources, and organizational behavior. a holistic approach that includes both clinical competencies. Key domains include stewardship, resource management. also role economics, policy implications, data analytics performance improvement. advocates incorporation education curricula ongoing professional development cultivate new generation capable thriving complex environments.

Язык: Английский

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Integrative analysis of AI-driven optimization in HIV treatment regimens

Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(6), С. 1314 - 1334

Опубликована: Июнь 7, 2024

The integration of artificial intelligence (AI) into HIV treatment regimens has revolutionized the approach to personalized care and optimization strategies. This study presents an in-depth analysis role AI in transforming treatment, focusing on its ability tailor therapy individual patient needs enhance outcomes. AI-driven involves utilization advanced algorithms computational techniques analyze vast amounts data, including genetic information, viral load measurements, history. By harnessing power machine learning predictive analytics, can identify patterns trends data that may not be readily apparent human clinicians. One key benefits is personalize based characteristics disease progression. considering factors such as drug resistance profiles, comorbidities, lifestyle factors, recommend most effective well-tolerated options for each patient, leading improved adherence clinical Furthermore, enables continuous monitoring adjustment real time, allowing healthcare providers respond rapidly changes status evolving dynamics. proactive management help prevent failure development resistance, ultimately better long-term outcomes patients. Despite transformative potential, without challenges. Ethical considerations, privacy concerns, need robust validation regulatory oversight are all important must addressed ensure safe implementation practice. In conclusion, integrative presented this underscores significant impact personalization regimens. leveraging technologies, approaches needs, quality life people living with HIV. Keywords: Integrative Analysis, AI- Driven, Optimization, Treatment, Regimens.

Язык: Английский

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Innovations in real-time infectious disease surveillance using AI and mobile data

International Medical Science Research Journal, Год журнала: 2024, Номер 4(6), С. 647 - 667

Опубликована: Июнь 6, 2024

The integration of artificial intelligence (AI) and mobile health data has ushered in a new era real-time infectious disease surveillance, offering unprecedented insights into dynamics enabling proactive public interventions. This paper explores the innovative applications AI transforming traditional surveillance systems for diseases. By harnessing power algorithms, coupled with vast amount generated from devices, researchers authorities can now monitor outbreaks greater accuracy efficiency. AI-driven predictive models analyze diverse datasets, including demographic information, travel patterns, social media activity, to detect early signs emergence predict potential outbreaks. use provides wealth information that was previously inaccessible methods. Mobile apps, wearables, other connected devices enable continuous monitoring individuals' indicators, allowing detection symptoms rapid response threats. Furthermore, geolocation facilitates tracking population movements identification high-risk areas transmission. However, this approach also presents challenges ethical considerations. Privacy concerns regarding collection must be carefully addressed ensure rights are protected. Additionally, issues related quality, interoperability, algorithm bias need mitigated reliability effectiveness systems. In conclusion, holds immense promise revolutionizing surveillance. leveraging these technologies, gain valuable dynamics, enhance capabilities, implement targeted interventions prevent spread it is essential address considerations associated its responsible effective implementation. Keywords: Innovations, Real-Time Infectious Disease, Surveillance, AI, Data.

Язык: Английский

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The Role of Leadership in Advancing Inclusive Health Technologies

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 203 - 220

Опубликована: Фев. 25, 2025

The rapid evolution of health technologies presents an unprecedented opportunity to create more inclusive and equitable healthcare systems. However, the successful implementation adoption these depend largely on effective leadership. This chapter explores pivotal role that leadership plays in advancing technologies, emphasizing need for visionary, ethical, culturally sensitive approaches. By examining case studies models, highlights strategies can foster inclusivity innovation, ensuring technological advancements benefit all members society, particularly those from marginalized communities.

Язык: Английский

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A longitudinal macro analysis of social determinants of health and their impacts on HIV prevalence and nutritional deficiencies in Sub-Saharan Africa

Acta Psychologica, Год журнала: 2025, Номер 255, С. 104869 - 104869

Опубликована: Март 14, 2025

Язык: Английский

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Navigating the Intersection of Diversity and Innovation

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 21 - 48

Опубликована: Апрель 11, 2025

The connection between diversity and innovation is essential for transformative leadership, mainly in today's global workplaces where diverse perceptions drive creativity flexibility. This chapter investigates how leaders can control cultural, generational, cognitive to improve attain competitive advantages. By accepting perceptions, foster a culture inclusivity flourish, letting firms respond dynamic market requirements technological enhancements. Efficient leadership needs deep comprehension of cultural nuances, emotional intelligence, commitment creating comprehensive environment all voices are respected. Leaders who endorse shared vision while inspiring exceptional contributions help bridge divides turn into strategic asset. also addresses the particular role overcoming issues linked with remote cross-cultural team management.

Язык: Английский

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Developing predictive models for HIV Drug resistance: A genomic and AI approach

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 521 - 543

Опубликована: Май 5, 2024

This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With increasing prevalence drug-resistant strains HIV, there is critical need for innovative strategies predict manage mutations, thereby optimizing treatment outcomes prolonging efficacy antiretroviral therapy (ART). Drawing on advances in genomics AI, this study outlines conceptual framework that can identify potential drug-resistance mutations genomes inform clinical decision-making. The proposed integrates from HIV-infected individuals with AI algorithms capable learning complex patterns within data. By analyzing sequences obtained HIV-positive patients, aim genetic variations associated resistance, likelihood development, guide selection appropriate regimens. holds promise personalized medicine care, enabling clinicians tailor based an individual's profile risk resistance. Key components include preprocessing extract relevant features, model training using machine techniques such as deep ensemble methods, validation performance cross-validation independent testing. Furthermore, integration data, history viral load measurements, enhances accuracy provides valuable insights into response dynamics.The represents paradigm shift offering proactive management surveillance. technologies, healthcare providers anticipate address emerging before they compromise efficacy. Ultimately, implementation improve patient outcomes, reduce transmission strains, advance global fight against HIV/AIDS. Keywords: Developing, Predictive Models, Drug Resistance, Genomic, Approach.

Язык: Английский

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Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 558 - 578

Опубликована: Май 5, 2024

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains significant challenge. This review explores effectiveness challenges of using machine learning (ML) for forecasting spread high-risk areas. ML models have shown promise identifying patterns trends data, enabling more accurate predictions targeted interventions. insights into outbreak leverage various data sources, including demographic, epidemiological, behavioural data. By analysing these algorithms can identify populations geographical areas susceptible to transmission. information is crucial public health authorities allocate resources efficiently implement preventive measures effectively. Despite potential benefits, several exist predictions. These include quality issues, such as incomplete or inaccurate which affect reliability Additionally, complexity transmission dynamics need real-time pose models. To address challenges, researchers practitioners are exploring innovative approaches, integrating multiple sources advanced techniques. Collaborations between researchers, officials, technology experts also developing robust In conclusion, while offers valuable addressing model essential its effective use. overcoming has significantly improve prevention efforts ultimately reduce burden region. Keywords: Machine Learning, AI, Outbreaks: Predictions, Insights.

Язык: Английский

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Leveraging machine learning for vaccine distribution in resource-limited settings: A synthesis of approaches

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 544 - 557

Опубликована: Май 5, 2024

Vaccine distribution in resource-limited settings remains a crucial global health challenge, exacerbated by factors such as inadequate infrastructure, limited resources, and complex supply chains. Leveraging machine learning (ML) holds promise for optimizing efficiency ensuring equitable access to life-saving vaccines. This paper synthesizes various ML approaches aimed at addressing vaccine challenges resource-constrained environments. The literature review examines existing research on applications healthcare distribution, highlighting key findings methodologies. Methodologically, criteria were established selecting relevant studies, with focus techniques their effectiveness contexts. Key identified include predictive analytics demand forecasting, route optimization algorithms efficient delivery, decision support systems prioritizing efforts. Case studies illustrate successful implementations real-world settings, showcasing improved coverage reduced wastage. Despite promising results, persist, including data scarcity, model generalization, ethical considerations. Future directions enhancing collection methods, refining specific contexts, integrating solutions into systems. In conclusion, this synthesis underscores the transformative potential of revolutionizing settings. By logistical barriers resource allocation, ML-driven offer pathway towards achieving universal immunization mitigating impact infectious diseases vulnerable populations. Keywords: Machine Learning, Distribution, Resource-Limited Settings, Synthesis Approaches.

Язык: Английский

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