Complex Health Research Articles

Integrating the health belief model into health education programs in a clinical setting.

The article demonstrates that health belief model (HBM)-based health education in hypertensive patients effectively improves blood pressure control and medication adherence at 3 months and 6 months. The HBM addresses perceived barriers, benefits, susceptibility, severity, and self-efficacy, leading to better health behaviors. HBM-based education has been effective in various contexts, including managing chronic diseases, promoting cancer screenings, and preventing infectious diseases. However, the model has limitations, such as cultural applicability and addressing complex health behaviors influenced by environmental factors. Future research should integrate HBM with other theories and conduct longitudinal studies to assess long-term impacts. Despite these limitations, HBM-based education significantly improves patient outcomes, highlighting its potential in health education and promotion when appropriately adapted and implemented. This reinforces the model's value in designing effective health interventions and advancing public health.

Community-Based Nursing and Public Health: A Synergistic Approach to Health Promotion

Community-based nursing and public health are pivotal in addressing the complex health needs of populations, particularly in underserved and vulnerable communities. This article examines the synergistic relationship between these fields and their collective impact on health promotion. Employing a mixed-methods approach, the study combines qualitative insights from community-based nurses with quantitative analysis of health outcomes in three diverse communities. Results indicate that collaboration between community-based nursing and public health improves health literacy, enhances access to care, and reduces preventable health disparities. Key challenges, such as resource constraints and workforce shortages, are discussed alongside opportunities for leveraging technology and policy support to strengthen these partnerships. The findings underscore the transformative potential of integrating nursing expertise with public health strategies to achieve sustainable health outcomes.

Perspectives of Digital Health Innovations in Low- and Middle-Income Health Care Systems From South and Southeast Asia.

Digital health innovations have emerged globally as a transformative force for addressing health system challenges, particularly in resource-constrained settings. The COVID-19 pandemic underscored the critical importance of these innovations for enhancing public health. In South and Southeast Asia, a region known for its cultural diversity and complex health care landscape, digital health innovations present a dynamic interplay of challenges and opportunities. We advocate for ongoing research built into system development and an evidence-based strategy focusing on designing and scaling national digital health infrastructures combined with a vibrant ecosystem or "marketplace" of local experiments generating shared experience about what works in which settings. As the global digital health revolution unfolds, the perspectives drawn from South and Southeast Asia-including the importance of local partnerships-may provide valuable insights for shaping future strategies and informing similar initiatives in low- and middle-income countries, contributing to effective digital health strategies across diverse global health contexts.

Prevalence of work-related injuries and associated factors among municipal solid waste collectors in Hawassa City, Sidama, Ethiopia 2023

Work-related injury is one of the most complex public health challenges globally. Rapid population growth and development in Hawassa City have resulted in increased solid waste generation, a growing complexity of waste with an increasing number of waste collectors, and mounting risks. This study aimed to assess the prevalence and associated factors of work-related injuries among solid waste collectors in Hawassa City, Sidama Regional State, Ethiopia, in 2023. A cross-sectional design was employed with 240 waste collectors using simple random sampling. Data were collected using the Open Data Kit and analyzed using the Statistical Package for Social Sciences version 24. Descriptive and logistic regression analyses were conducted, and associations were determined using odds ratios (ORs) with a 95% confidence interval (CI) and a significance level of 0.05. The 240 waste collectors, 237 (98.8%) participated in this study. Among them, 104 (43.9%, 95% CI: 37.5–50.3) reported work-related injuries. Factors such as work duty as a recycler (AOR = 6.64; 95% CI: (2.11 to 20.845)), manual handling of waste (AOR: 5.187; 95% CI (2.23 to 12.04)), receiving first occupational health training ((AOR: 3.449; 95% CI (1.328 to 8.96)), on-the-job training (AOR = 7.826; 95% CI (3.08 to 19.839)), and the use of modern waste handling equipment (AOR: 0.251; 95% CI, (0.071 to 0.893)) were found to be significant predictors of work-related injury. The prevalence of work-related injuries among municipal solid waste collectors in Hawassa City is high. Thus, we recommend that pre-employment and on-the-job training be provided to waste collectors, along with the regular provision of appropriate waste-handling equipment.

Sexual and Gender Diverse Healthcare Navigation Model: A Community-Participatory Delphi Adaptation of the Transitional Care Model for Sexual and Gender Diverse Individuals

ABSTRACT Health care transitions, movements between providers or settings, can lead to poor outcomes, higher resource utilization, and even higher costs. Health care transitions can be complex experiences for sexual and gender diverse (SGD) individuals, especially when there is a need for ongoing health care (e.g. gender-affirming therapy, depression). Engaging new providers can lead to setbacks in trust requiring self-disclosure SGD-identities all over again. The Transitional Care Model (TCM) supports improvement of outcomes associated with health care transitions; however, it has not been tailored to address the unique needs of SGD populations. A sample of adult SGD patients (n = 17) and SGD health experts (n = 11) from across the US completed a six cycle, electronic, modified Delphi process to adapt the TCM for the SGD population. The iterative process produced a TCM for SGD populations including eight refined and renamed components with SGD-specific descriptions and exemplars. The resulting model, the Sexual and Gender Diverse Healthcare Navigation Model, provides an eight-component adaptive model of transitional care that centers SGD patients and their support systems across complex health care systems. The consensus panel provided descriptions for each of the eight resulting components—identifying three foundational components—the right staffing, awareness of needs and risks, and engaging individuals and their support systems throughout transitions in care. This model stands to support improved health outcomes among SGD through a multi-component, patient centered approach to health care navigation.

Theoretical perspectives on biostatistics and its multifaceted applications in global health studies

Biostatistics plays a crucial role in global health studies by providing the tools necessary for analyzing complex health data and informing evidence-based public health decisions. This theoretical review explores the foundations of biostatistics and its diverse applications in the context of global health. At its core, biostatistics utilizes mathematical and statistical principles to analyze health data, enabling researchers to identify patterns, determine risk factors, assess interventions, and predict health outcomes across populations. Key theoretical concepts, including probability theory, statistical inference, and regression models, form the backbone of biostatistical analysis and are integral to understanding the dynamic relationships between health determinants and outcomes. The review highlights the various applications of biostatistics in global health, focusing on areas such as disease surveillance, epidemiology, and public health intervention evaluation. Biostatistical techniques are essential for analyzing epidemiological data, monitoring disease trends, and evaluating the effectiveness of health policies and interventions on a global scale. Furthermore, advanced statistical methods, including machine learning and Bayesian approaches, are becoming increasingly important in global health research. These methods allow for the analysis of large-scale data, predictive modeling, and more precise risk assessments, particularly in the context of emerging diseases, genetic research, and environmental health studies. Despite its strengths, biostatistics faces challenges in global health, including issues related to data quality, ethical considerations, and cross-cultural variations in health outcomes. Addressing these challenges is critical for ensuring accurate and equitable health assessments. Looking forward, biostatistics continues to evolve with innovations in data science, big data analytics, and real-time health data collection, making it an indispensable tool for improving global health outcomes and supporting health policies. This review emphasizes the theoretical underpinnings of biostatistics while highlighting its transformative potential in global health research and practice. Keywords: Theoretical Perspectives, Biostatistics, Multifaceted Applications, Global Health Studies.

Nurse Faculty Perceptions of Anxiety in Nursing Students.

Anxiety in nursing students can interfere with the development of the knowledge, skills, and clinical judgment necessary to practice in a complex health care environment. Thirteen faculty from nursing programs across one state were interviewed to explore faculty perceptions of anxiety among prelicensure baccalaureate nursing students. Data analysis from this qualitative study showed that faculty recognized anxiety in students and its contributing factors. Findings also revealed that participants were unsure of the levels of anxiety that hinder or promote student learning but believed faculty are in a position to help students manage these feelings.

Characteristics and Readmission Risks Following Sepsis Discharges to Home

Objective: To examine the characteristics and risk factors associated with 30-day readmissions, including the impact of home health care (HHC), among older sepsis survivors transitioning from hospital to home. Research Design: Retrospective cohort study of the Medical Information Mart for Intensive Care (MIMIC)-IV data (2008–2019), using generalized estimating equations (GEE) models adjusting for patient sociodemographic and clinical characteristics. Subjects: Sepsis admission episodes with in-hospital stays, aged over 65, and discharged home with or without HHC were included. Measures: The outcome was all-cause hospital readmission within 30 days following sepsis hospitalization. Covariates, including the primary predictor (HHC vs. Home discharges), were collected during hospital stays. Results: Among 9115 sepsis admissions involving 6822 patients discharged home (66.8% HHC, 33.2% Home), HHC patients, compared with those discharged without services, were older, had more comorbidities, longer hospital stays, more prior hospitalizations, more intensive care unit admissions, and higher rates of septic shock diagnoses. Despite higher illness severity in the HHC discharges, both groups had high 30-day readmission rates (30.2% HHC, 25.2% Home). GEE analyses revealed 14% higher odds of 30-day readmission for HHC discharges after adjusting for risk factors (aOR: 1.14; 95% CI: 1.02–1.27; P=0.02). Conclusions: HHC discharges experienced higher 30-day readmission rates than those without, indicating the need for specialized care in HHC settings for sepsis survivors due to their complex health care needs. Attention to sepsis survivors, regardless of HHC receipt, is crucial given the high readmission rates in both groups. Further research is needed to optimize postacute care/interventions for older sepsis survivors.

The role of Alberta pharmacists working in opioid use disorder and their potential to prescribe buprenorphine-naloxone: A qualitative study.

Opioid toxicity continues to have significant morbidity and mortality in Alberta. Opioid agonist therapy is an effective treatment for opiate use disorder (OUD), with first-line treatment with buprenorphine-naloxone (BUP-NAL) being both highly effective and safe. Barriers to care limit access to treatment, and more access points for treatment are needed. Pharmacists in Alberta have a wide scope of prescribing authority and high accessibility. This study describes the barriers to care and the roles of pharmacists engaged in OUD treatment, and explores the potential for pharmacists to prescribe BUP-NAL to improve access to care. Semistructured interviews were conducted with pharmacists from Alberta in January 2024. Key informants were identified using professional networks and the reverse snowball method, and continued until data saturation. Thematic analysis was conducted by 2 investigators using open coding. Ten pharmacists were interviewed, and 4 major themes emerged: barriers to access OUD treatment, the current role of pharmacists in caring for patients with OUD, the future role of pharmacists as prescribers of BUP-NAL, and enabling pharmacists to prescribe. Patients experience many barriers to care, and a complex health system contributes to this. Pharmacists working with patients with OUD are highly knowledgeable and involved in assessing, managing, and monitoring therapy in a multidisciplinary capacity. Extending authority for pharmacists to prescribe BUP-NAL can improve access to care but must consider collaboration and social context. Pharmacists are skilled and positioned to improve access to care for patients with OUD needing BUP-NAL.

Mathematical modeling and computational analysis of hepatitis B virus transmission using the higher-order Galerkin scheme

Abstract Hepatitis B, a liver disease caused by the hepatitis B virus (HBV), poses a significant public health burden. The virus spreads through the exchange of bodily fluids between infected and susceptible individuals. Hepatitis B is a complex health challenge for individuals. In this research, we propose a nonlinear HBV mathematical model comprising seven compartments: susceptible, latent, acutely infected, chronically infected, carrier, recovered, and vaccinated individuals. Our model investigates the dynamics of HBV transmission and the impact of vaccination on disease control. Using the next-generation matrix approach, we derive the basic reproduction number R 0 {R}_{0} and determine the disease-free equilibrium points. We establish the global and local stability of the model using the Lyapunov function. The model is numerically solved using the higher-order Galerkin time discretization technique, and a comprehensive sensitivity analysis is carried out to investigate the impact of all physical parameters involved in the proposed nonlinear HBV mathematical model. A comparison was made of the accuracy and dependability with the findings produced using the Runge–Kutta fourth-order (RK4) approach. The findings highlight the critical need for vaccination, particularly among the exposed class, to facilitate rapid recovery and mitigate the spread of HBV. The results of this study provide valuable insights for public health policymakers and inform strategies for hepatitis B control and elimination.

Concepts of Implied Interventions for Treating Depression: A Comprehensive Review

Hence, answering the question how a person is able to secure and maintain lifelong good mood would facilitate in deceiphering depression; a common and etiologically complex multi factorial mental health disorder which affects over 264 m individuals worldwide with tremendous effect on morbidity and economic burden. Here, our study results from more than 100 experimental and observational studies with respect to effective treatments for depression across three main groups such as: Pharmacological interventions, Psychotherapeutic approaches, and Emerging digital health solutions have been effective as implied interventions to tret Depression. While antidepressants, particularly selective serotonergic reuptake inhibitors (SSRIs), continued as the bedrock of therapy; Novel therapies with Ketamine have shown potential for treatment failure cases. Large-scale studies of Psychotherapeutic methods, such as Cognitive Behavioural Therapy (CBT) and Interpersonal Therapy (IPT), show high effectiveness rates in most populations. Digital health interventions, such as e-therapy, were potential alternatives and particularly suitable for patients in rural areas. Integrating these modalities will allow clinicians to offer more individualised and tailored care. This review provided an overview of the mechanisms of each intervention for depression as a clinical aid to inform decisions by clinicians as well as policy makers seeking to improve treatment pathways for individuals with Mood disorders and Depression.

A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies.

We propose that one of the main hurdles in delivering comprehensively informed care results from the challenges surrounding the extraction, representation, and retention of prior clinical experience and basic medical knowledge, as well as its translation into time- and context-informed actionable interventions. While emerging applications in artificial intelligence-based techniques, for example, large language models, offer impressive pattern association capabilities, they often fall short in producing human-readable explanations crucial to their integration into clinical care. Moreover, they require large well-defined and well-integrated data sets that typically conflict with the availability of such data in all but a few areas of medicine, for example, medical imaging and neuroimaging, noninvasive monitoring of bio-electrical activity, etc. In this chapter, we argue that approximate reasoning rooted in the knowledge that is explainable to the human clinician may offer attractive avenues for the introduction of such knowledge in a systematic way that supports formal retention, sharing, and reuse of new clinical and basic medical experience. We outline a conceptual protocol that targets the use of sparse and disparate data of different types and from different sources, seamlessly drawing on our collective experience and that of others. We illustrate the utility of such an integrative approach by applying the latter to the assessment and reconciliation of data from different experimental models, human and animal, in the example use case of a complex health condition.

Subjective well-being and mental health during the COVID-19 pandemic in Hungary

Purpose This study aims to examine the impact of COVID-19 pandemics on subjective well-being (SWB) and mental health in Hungary, with a particular focus on the roles of socioeconomic and COVID-19-related factors in shaping these outcomes. Design/methodology/approach A cross-sectional online survey was conducted among 2,000 Hungarian adults from May 25 to June 8, 2021. The data was collected using standardized questionnaires, including the SWLS, GAD-7 and PHQ-9. Correlation analysis, one-way ANOVA and linear regression were used. Findings The study found significant anxiety (41.5%), depression (48.1%) and life dissatisfaction (41.9%) among 2,000 participants. Females reported higher anxiety and depression than males. Single individuals had the lowest life satisfaction. Higher income levels are correlated with greater life satisfaction. Quarantine and vaccination were linked to higher life satisfaction and lower psychological distress. The regression showed that older age, being married and higher income were linked to lower depression and anxiety. Severity of infection and quarantine increased both depression and anxiety. Infection reduced life satisfaction, but severe cases showed a slight improvement in satisfaction, highlighting complex health and well-being relationships. Practical implications The findings reveal complex relationships, including unexpected resilience in severe COVID-19 cases. Originality/value This study offers insights into the impact of the COVID-19 pandemic on SWB and mental health in Hungary. By analyzing socioeconomic, demographic and pandemic-related factors in a large sample, it highlights complex relationships between quarantine, infection severity, vaccination and well-being. The findings contribute to understanding how these factors influence mental health, informing targeted interventions during health crises.

The weight of office? A scoping review of mental health issues and risk factors in elected politicians across democratic societies.

The mental health and capacity to govern of democratically-elected politicians have become burgeoning topics of interest. Notably, in fulfilling demanding and high-stress roles, political officeholders could encounter distinctive risk factors, yet existing research literature about these subpopulations remains underexplored. This scoping review aimed to systematically examine the breadth of available evidence on mental health issues and risk factors affecting democratically-elected politicians internationally and to identify future research needs. Using pre-defined eligibility criteria based on JBI guidelines, a systematic keyword search was conducted in May 2024 of MEDLINE, Scopus, and APA PsycNet, supplemented by snowballing techniques. Only those studies reporting primary, empirical evidence on mental ill-health or risk factors with psychological correlates from serving politicians in "Full" or "Flawed" democracies (per Democracy Index) were included from 1999 to 2024. Titles and abstracts were screened and the full-texts of potentially eligible literature were assessed before extraction and synthesis. Eighteen sources met the eligibility criteria, cumulatively encompassing ~3,500 national, state, and municipal politicians across seven democracies (Australia, Canada, the Netherlands, Norway, New Zealand, the United Kingdom, and the United States). Cross-sectional surveys were predominantly utilized, with lesser use of mixed-methods approaches, qualitative interviews, and longitudinal cohorts. Violence emerged as a key concept, with twelve sources (66.7%) underlining its psychological toll and certain data indicating a disproportionate impact on female officeholders. Furthermore, four sources (22.2%) explored general psychopathology trends, revealing varying but sizeable mental ill-health and high-risk alcohol consumption rates, and two studies (11.1%) demonstrated adverse effects from specific occupational conditions. Current literature suggests that democratically-elected politicians can face complex mental health challenges. However, significant research gaps remain, including a paucity of prevalence estimates, longitudinal data, and intervention studies. Equally, the underrepresentation of most democratic countries accentuates the need for a more diverse evidence-base to better support the mental wellbeing of politicians worldwide.

Examining Macro-Level Barriers and Facilitators to Scaling Up Integrated Care from a Complexity Perspective: A Multi-Case Study of Cambodia, Slovenia, and Belgium.

The 'Scale-Up diaBetes and hYpertension care' (SCUBY) project provides evidence on scaling-up integrated care (IC) in Cambodia, Slovenia, and Belgium. This paper examines macro-level barriers and facilitators to scaling up IC in these settings. We used a multi-case study design, with each country being a case. Document review, focus groups, and stakeholder interviews were conducted. The WHO health system building blocks guided the thematic analysis. We then visualised and examined the interlinkages between barriers in each country. Common challenges to scaling up IC across the three health systems relate to: governance and leadership; health workforce; inadequate health financing system; and fragmented health information systems. In Cambodia, access to non-communicable disease (NCD) services and medicine are important issues. IC scale-up is facilitated by its strong governance and public health service model in Slovenia but health workforce shortages risk progress. In Belgium, the fragmented governance system and predominant fee-for-service provider payment are important barriers. A common response to health workforce and workload challenges was task shifting: to primary care nurses in Belgium, peer supporters in Slovenia, and community health workers in Cambodia. Examining differences and similarities between barriers in each health system stimulated reciprocal learning. Interactions between health system barriers in specific contexts require further attention to move complex health systems forward.

Multiple feature selection based on an optimization strategy for causal analysis of health data

PurposeRecent advancements in information technology and wearable devices have revolutionized healthcare through health data analysis. Identifying significant relationships in complex health data enhances healthcare and public health strategies. In health analytics, causal graphs are important for investigating the relationships among health features. However, they face challenges owing to the large number of features, complexity, and computational demands. Feature selection methods are useful for addressing these challenges. In this paper, we present a framework for multiple feature selection based on an optimization strategy for causal analysis of health data.MethodsWe select multiple health features based on an optimization strategy. First, we define a Weighted Total Score (WTS) index to assess the feature importance after the combination of different feature selection methods. To explore an optimal set of weights for each method, we design a multiple feature selection algorithm integrated with the greedy algorithm. The features are then ranked according to their WTS, enabling selection of the most important ones. After that, causal graphs are constructed based on the selected features, and the statistical significance of the paths is assessed. Furthermore, evaluation experiments are conducted on an experiment dataset collected for this study and an open dataset for diabetes.ResultsThe results demonstrate that our approach outperforms baseline models by reducing the number of features while improving model performance. Moreover, the statistical significance of the relationships between features uncovered through causal graphs is validated for both datasets.ConclusionBy using the proposed framework for multiple feature selection based on an optimization strategy for causal analysis, the number of features is reduced and the causal relationships are uncovered and validated.

From pilot to national roll-out of the improved Community Health Fund (iCHF) in Tanzania: lessons learnt and way forward

IntroductionScaling up public health interventions in the health systems of resource poor settings come with technical and operational challenges. Little is documented on scaling up complex health financing interventions and their related outcomes, especially the voluntary health insurance schemes. This study aimed to analyse the scale-up steps, successes and challenges of the improved community health fund (iCHF), a voluntary health insurance scheme in Tanzania,MethodsIn this paper, guided by the Expand Net framework (a scale-up framework for health system interventions), we present a systematic analysis of countrywide scale-up of the iCHF that started in 2019 and implemented in partnership between the government and development partners. We systematically collected information on the scale-up steps and the success and challenges. The collected data was analysed using descriptive statistics.ResultsThe scale-up involved multiple steps and actions at different levels of the health system. The initial step involved gathering stakeholders’ views on scale-up options and strategies. The subsequent steps focused on mobilizing resources for scale-up, advocacy and promotion of the scheme through media, community leaders and role models, capacity building to implementing organs, institutionalizing the scale-up processes, intensifying the scale-upscale-up activities for expansion and spontaneous scale-up and technical backstopping to lower levels of the health system on the scale-up process. We found success and challenges as the scale-upscale-up progressed to mature stages. The success included acceptability and institutionalization of the scale-up activities and growing enrolments and funds in the scheme. The challenges included: the costs to sustaining advocacy and enrolments, equity in scale-upscale-up activities across regions, relying on top-down scale-upscale-up approaches, influence of contextual factors and lack of implementation research alongside the scale-upscale-up process.ConclusionThis paper underscores the scale up steps and success and challenges of scaling-up a voluntary health insurance scheme in a resource-constrained health system. Sustaining the scale-upscale-up gains will require utilizing program data and experiences to sustainably improve the scheme performance while also harnessing support from stakeholders. Further research is needed to assess equity and quality of outcomes of the scale up.

Reduction in Restraint and Critical Incidents in a Norwegian Residential Treatment Facility for Children Aged 7–13 Following the Implementation of the Neurosequential Model of Therapeutics

In child and adolescent inpatient, residential and day-treatment facilities, the use of physical restraints and the occurrence of critical incidents are a significant problem. Restraints may sometimes be necessary if a child exhibits dangerous aggressive behavior, but may also be misused or overused, and have been shown to be preventable in many cases. This study aims to investigate if the implementation of the Neurosequential Model of Therapeutics (NMT) has an effect on the annual number of physical restraint incidents in a residential treatment facility for children with complex mental health disorders. Data before and after NMT implementation were collected from the agency’s restraint records. The results showed that post-NMT implementation, there was a substantial and sustained reduction in restraint incidents, with a Cohen’s d value of 2.03, indicating a very large effect. Limiting restraint use in treating children with complex mental health disorders can foster a safer and more therapeutic environment, with potential improvements in treatment outcomes. This study demonstrated a substantial drop in restraint incidents following the implementation of the Neurosequential Model in a residential facility for children aged 7–13. This reduction is presumably pivotal for children with complex mental health disorders, making treatment less coercive and offering promise for settings in which restraint incidents are a concern.

Multidisciplinary Approaches in General Medical Practice: Enhancing Collaboration for Better Patient Care

Multidisciplinary approaches in general medical practice have become integral to providing comprehensive, patient-centered care, as the complexity of modern health issues often requires a diverse range of expertise. This review explores the role and benefits of multidisciplinary teams in enhancing patient outcomes, improving resource efficiency, and increasing patient satisfaction in general medical settings. By examining the contributions of various healthcare professionals—including primary care physicians, specialists, allied health professionals, and behavioral health specialists—this review emphasizes how collaborative efforts address complex health needs more effectively than traditional, siloed approaches. Key case studies illustrate successful multidisciplinary practices in managing chronic diseases and complex conditions, highlighting the advantages of coordinated care models. The review also addresses challenges, such as communication barriers, hierarchical dynamics, and logistical constraints, that can hinder effective collaboration. Furthermore, it discusses technological supports, such as electronic health records (EHR) and telemedicine, which facilitate real-time communication and care coordination. The article concludes with recommendations for expanding multidisciplinary teams in primary care, promoting educational initiatives for team-based care skills, and advocating for policy support to institutionalize multidisciplinary approaches in general medicine. These findings suggest that well-coordinated multidisciplinary teams can transform patient care, making it more holistic, efficient, and responsive to individual needs.

Insights on therapeutic approaches of natural anti-Alzheimer's agents in the management of Alzheimer's disease: A future perspective.

In the current scenario, Alzheimer's disease is a complex, challenging, and arduous health issue, and its prevalence, together with comorbidities, is accelerating around the universe. Alzheimer's disease is becoming a primary concern that significantly impacts an individual's status in life. The traditional treatment of Alzheimer's disease includes some synthetic drugs, which have numerous dangerous side effects, a high risk of recurrence, lower bioavailability, and limited treatment. Hence, the current article is a detailed study and review of all known information on plant-derived compounds as natural anti-Alzheimer's agents, including their biological sources, active phytochemical ingredients, and a possible mode of action. With the help of a scientific data search engine, including the National Center for Biotechnology Information (NCBI/PubMed), Science Direct, and Google Scholar, analysis from 2001 to 2024 has been completed. This article also described clinical studies on phytoconstituents used to treat Alzheimer's disease. Plant-derived compounds offer promising alternatives to synthetic drugs in treating Alzheimer's disease, with the potential for improving cognitive function and slowing down the progression of the disease. Further research and clinical trials are needed to fully explore their therapeutic potential and develop effective strategies for managing this complex condition.