Efficient Health Care System Research Articles (Page 1)

The growing demand for accessible, efficient, and data-driven healthcare systems has encouraged the development of digital solutions addressing maternal and infant health challenges. In response, the researchers developed WOMB: A Web-Based System for Maternity Support and Infant Health Tracking with Integrated Data Analytics and Smart Algorithms, a platform designed to assist mothers and healthcare professionals in monitoring health data, improving record accuracy, and enhancing communication. The WOMB system is a centralized, user-friendly web platform that strengthens the management of maternal and infant healthcare. It provides essential functions such as tracking infant growth, scheduling medical appointments, managing health records, and offering educational resources for both mothers and healthcare workers. Through data analytics and intelligent algorithms, the system generates predictive insights, automates reminders, and supports evidence-based healthcare decisions. Built using PHP, MySQL, HTML, CSS, and JavaScript, the system complies with ISO/IEC 25010 Software Quality Standards to ensure security, usability, functionality, and performance efficiency. Its beneficiaries include mothers, infants, and healthcare providers, as it promotes improved health tracking, organized digital record management, and better access to critical information. ISO/IEC 25010 is an international software quality standard that defines attributes such as functionality, reliability, and usability to evaluate software effectiveness. This study utilized an Applied Research Design and adopted the Waterfall Model of the System Development Life Cycle (SDLC) to guide system creation and evaluation. The model involved six key phases—requirement analysis, system design, implementation, testing, deployment, and maintenance—to ensure structured and high-quality development. Data were collected through surveys, interviews, and observations from selected healthcare professionals and mothers to identify their needs and challenges in managing maternal and infant healthcare records. The Software Development Life Cycle (SDLC) is a structured process for planning, creating, testing, and deploying an information system efficiently. A total of 60 respondents participated in the system evaluation, consisting of 40 user respondents (mothers and healthcare providers) and 20 technical respondents (IT specialists and system developers). They assessed the system using the ISO/IEC 25010 Software Quality Model, focusing on the attributes of functionality, reliability, usability, efficiency, maintainability, portability, and security. Statistical tools such as the weighted mean were applied to analyze the results, which showed overall average means of 3.50 for user respondents and 3.63 for technical respondents—both interpreted as Strongly Agree. ISO/IEC 25010 is an international software quality standard that defines attributes such as functionality, reliability, and usability to evaluate software effectiveness. The evaluation results show that respondents found the system functional, secure, and easy to use for managing maternal and infant data. Usability received the highest rating among users, while functionality ranked highest among technical respondents. Although reliability obtained slightly lower ratings, it remained positive, suggesting only minor areas for improvement. Overall, the findings affirm that WOMB effectively meets international software quality standards and fulfills its purpose of promoting digital innovation in maternal and infant healthcare.

Background: Artificial Intelligence (AI) holds significant potential to enhance operational efficiency and quality in healthcare. However, despite substantial investment, its widespread, sustained implementation is limited, necessitating a thorough risk assessment to overcome current adoption barriers. Methods: This scoping review, guided by the Arksey and Malley framework, systematically mapped 13 articles published between 2019 and 2024, sourced from five major databases (including CINAHL, Medline, and PubMed). A rigorous, systematic process involving independent data charting and critical appraisal, using the Critical Appraisal Skills Programme (CASP) tool, was implemented, followed by thematic synthesis to address the research questions. Results: AI demonstrates a significant positive impact on both operational efficiency (e.g., optimised resource allocation, reduced waiting times) and patient outcomes (e.g., improved patient-centred, proactive care, and identification of readmission risks). Major implementation hurdles identified include high costs, critical data security and privacy concerns, the risk of algorithmic bias, and significant staff resistance stemming from limited understanding. Conclusions: Healthcare managers must address key challenges related to cost, bias, and staff acceptance to leverage the potential of AI fully. Strategic investments, the implementation of robust data governance frameworks, and comprehensive staff training are crucial steps for mitigating risks and creating a more efficient, patient-centred, and effective healthcare system.

Abstract Background Diabetes is a common chronic illness; it is estimated that over 38 million people in the USA all ages have diabetes and 8.17 million of them are not aware of it. Recently, diagnosing and monitoring diabetes has becoming depending on measuring A1C. A1C synthesizes from the attachment of hexose to hemoglobin molecule, which continues throughout the lifespan of the red blood cell, the more glucose in blood the more hemoglobin gets glycated or the higher A1C, and the levels does not change until the red blood cells die and get replaced with the younger ones, basically, A1c reflects the blood sugar control for the past 90 days or the lifespan of the red blood cell. Methods Data was collected from 52,800 specimens collected from residents at Long-Term Care Facilities in 2024; all samples were run for A1C using Roche Cobas C502. Patients were separated based on the specific time between orders for A1C. The data was separated based on time span between order days between testing. Patient data were separated further based on gender. The percentage of follow-up test within 90 and at any time was calculated. Statistical analyses were done using Analyse-it. Results women accounted for 54.7%; 33,563 patients had A1c done before, 7,149 of them had an A1C test done in less than 90 days. There was no statistical difference between men and women in the population we tested. Conclusion Laboratory tests are essential tools in patient management, but their overuse is becoming a growing concern, contributing to higher healthcare costs and reduced laboratory productivity due to the increased workload associated with running unnecessary tests, which includes phlebotomy time, reagents, supplies, and technologist time. Additionally, laboratories face reimbursement denials, which require additional staff for follow-up. Implementing test utilization stewardship is crucial and involves educating physicians on appropriate test ordering frequencies, conducting audits, providing feedback on ordering patterns, and setting rules to prevent unnecessary tests before sample collection. Such measures are essential for creating a more efficient healthcare system.

Abstract Access to healthcare is a fundamental right, yet disparities in healthcare facility distribution persist across sub-Saharan Africa. Rwanda serves as a compelling case study where the current resource allocation strategy uses population distribution density over actual healthcare needs, often leading to inefficiencies in service delivery. This imbalance can worsen healthcare disparities, especially in areas where the demand for medical services far exceeds the available resources. This research examines the alignment between healthcare facility distribution, disease prevalence trends, focusing on Malaria, Tuberculosis (TB), and HIV which are the top leading causes of mortality in Rwanda. By analyzing these healthcare data, this study evaluates whether the current healthcare infrastructure distribution strategy effectively meets regional and district-level demands. Additionally, it proposes a new model to optimize resource allocation and improve healthcare accessibility. To achieve this, we use a data-driven approach combining geospatial analysis, machine learning, and national health survey data. Geospatial analysis enables us to visualize and quantify disparities in healthcare accessibility, while machine learning techniques help identify patterns and correlations between disease prevalence, population distribution, and healthcare facility locations. By integrating these datasets, our model assesses the efficiency of the current allocation strategy and proposes an optimized distribution framework that better aligns healthcare facilities with regional health burdens. Our findings suggest that a more balanced distribution of healthcare facilities guided by real-time data rather than static population figures, could lead to improved health outcomes, reduced travel distances for patients, and a more efficient healthcare system overall. By providing a model that is adaptable and scalable, our research offers a framework that can be applied beyond Rwanda to other nations. Key messages • Access to healthcare is a fundamental right, yet disparities in healthcare facility distribution persist across developing nations. • A more balanced distribution of healthcare facilities guided by real-time data, could lead to improved health outcomes and a more efficient healthcare system overall.

BackgroundThe integration of artificial intelligence (AI) and advanced large language models in medical education and clinical practice is reshaping healthcare. These technologies have significant potential to enhance training experience and quality of life for medical residents. By automating routine tasks such as documentation and preliminary data analysis, AI-driven models can significantly reduce the workload, enabling residents to focus more on direct patient care and hands-on learning opportunities.Main BodyAI-driven support in diagnostics and decision-making may also reduce diagnostic errors, fostering a safer and more efficient healthcare environment. Furthermore, by alleviating administrative burdens, AI could play a critical role in mitigating resident burnout, contributing to a more resilient healthcare workforce and ultimately improving the continuity and quality of patient care. However, the adoption of AI in medical practice poses challenges. Automation risks reducing essential clinical skills, and over-reliance on AI may impact on professional autonomy and the development of diagnostic capacities. Concerns also persist regarding biased data, data security, legal issues, and the transparency in AI-driven decision-making processes.ConclusionAddressing these challenges requires collaboration among healthcare professionals, AI developers and policymakers, as well as ethical frameworks and country-specific regulations. Only through a balanced and collaborative approach can we unlock AI’s full potential to create a more efficient, equitable, and patient-centered healthcare system.

A novel energy efficient IoT healthcare system using integrated optimization with authentication mechanism

Women in anaesthesia and critical care: a narrative review of their impact on patient care, research innovation, and team performance.

Identifying strategies to enhance patient engagement and to control healthcare costs promotes a responsive and efficient healthcare system. The aim of this study is to predict healthcare cost savings associated with delivering telehealth advance care planning (ACP) to patients living with dementia. Two Geriatrics Workforce Enhancement Programs delivered training to primary care providers on using telehealth to provide ACP. Using electronic health records data from 6344 dual-eligible Medicare/Medicaid patients receiving telehealth primary care from trained providers in an urban safety net system, persons living with dementia (n = 401) were identified by extracting ICD-10 codes. The primary outcome was the estimated hospitalization-associated cost, with a key independent variable of ACP billing status. Multiple linear regressions and machine learning techniques estimated the impact of telehealth ACP on hospitalization-associated costs with a differential analysis by race. Compared to non-Hispanic Whites, hospitalization costs among Hispanic elders were higher by USD 14,232.40. Costs for non-English speakers or those having increased comorbidities were higher by USD 27,346.60 and USD 26,072.70, respectively. Overall, receiving ACP was associated with lower costs of USD 23,928.84. Dementia patients seen by primary care providers in a system receiving training to offer ACP via telehealth realized significant cost savings, with marked differences among those of non-White racial backgrounds.

Charting the Course: Exploring the Dynamic Impact of Mentorship Programs on Nurse Practitioner Satisfaction and Health Care Outcomes.

BackgroundPrimary healthcare plays a pivotal role in optimizing healthcare resource allocation and ensuring equitable access to services. In China, first-contact care within the tiered medical care system is essential for alleviating pressure on large hospitals and enhancing healthcare equity. However, challenges such as uneven resource distribution and low patient trust in primary institutions limit the effectiveness of first-contact care. This study aims to explore the dynamic interactions of key factors influencing first-contact effectiveness and provide evidence-based policy recommendations.MethodsA systematic approach was employed, integrating a literature review, qualitative interviews, and MICMAC structural analysis. Using Causal Loop Diagram (CLD) methodology, this study mapped the causal pathways and feedback mechanisms among factors such as health insurance awareness, medical consortium development, and tiered healthcare policy guidance. MICMAC analysis quantified the influence and dependence of these variables to identify key drivers within the system.ResultsThe study revealed that primary healthcare service quality, patient trust, and healthcare-seeking behaviors are are vital determinants for improving first-contact effectiveness. Key driving factors include health insurance awareness, medical consortium construction, and tiered healthcare policy guidance, which form reinforcing feedback loops to enhance system efficiency. The findings highlight the importance of resource sharing, trust-building, and policy support in optimizing the tiered medical care system.ConclusionsThis study provides a comprehensive framework for understanding the complexities of first-contact care within China’s healthcare system. The insights gained emphasize the need for targeted interventions, including promoting health insurance awareness, strengthening medical consortiums, and improving policy incentives. These recommendations can inform policy reforms aimed at achieving equitable and efficient healthcare systems globally.Trial registrationNot applicable, as this study does not involve a healthcare intervention on human participants.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-23884-w.

Machine learning (ML) and Internet of Things (IoT) play a vital role in various disciplines including, computational intelligence, artificial intelligence, healthcare systems and neural network, etc. In this study, the synergy of ML and IoT is successfully used for the creation of high-performance control components of healthcare systems, where comprehensive health data can be used for research and improving healthcare services. By linking medical networks, equipment, and facilities to the internet, the IoT is revolutionizing the healthcare sector, facilitating easy data interchange and improving patient care. IoT in healthcare primarily enhances patient outcomes through medical problem detection and monitoring. Many type of medical equipment, including glucose meters, are intended to capture patient information and symptoms. In the context of multi-criteria decision-making (MCDM), the criteria importance through intercriteria correlation (CRITIC) and combined compromise solution (CoCoSo) approaches have shown to be effective in calculating the weights of criteria and ranking of alternatives, respectively. Our main goal in this study is to introduce a novel approach for MCDM based on control parameters of linear Diophantine fuzzy sets (LDFSs). The suggested approach is practically applied for the selection of an efficient IoT healthcare system. In order to elucidate the validity, reliability, and efficiency in the patient care, a sensitivity analysis and comparative analysis are also carried out.

Pain is a multifaceted phenomenon that significantly affects a large portion of the global population. Objective pain assessment is essential for developing effective management strategies, which in turn contribute to more efficient and responsive healthcare systems. However, accurately evaluating pain remains a complex challenge due to subtle physiological and behavioural indicators, individual-specific pain responses, and the need for continuous patient monitoring. Automatic pain assessment systems offer promising, technology-driven solutions to support and enhance various aspects of the pain evaluation process. Physiological indicators offer valuable insights into pain-related states and are generally less influenced by individual variability compared to behavioural modalities, such as facial expressions. Skin conductance, regulated by sweat gland activity, and the heart's electrical signals are both influenced by changes in the sympathetic nervous system. Biosignals, such as electrodermal activity (EDA) and electrocardiogram (ECG), can, therefore, objectively capture the body's physiological responses to painful stimuli. This paper proposes a novel multi-modal ensemble deep learning framework that combines electrodermal activity and electrocardiogram signals for automatic pain recognition. The proposed framework includes a uni-modal approach (FCN-ALSTM-Transformer) comprising a Fully Convolutional Network, Attention-based LSTM, and a Transformer block to integrate features extracted by these models. Additionally, a multi-modal approach (CrossMod-Transformer) is introduced, featuring a dedicated Transformer architecture that fuses electrodermal activity and electrocardiogram signals. Experimental evaluations were primarily conducted on the BioVid dataset, with further cross-dataset validation using the AI4PAIN 2025 dataset to assess the generalisability of the proposed method. Notably, the CrossMod-Transformer achieved an accuracy of 87.52% on Biovid and 75.83% on AI4PAIN, demonstrating strong performance across independent datasets and outperforming several state-of-the-art uni-modal and multi-modal methods. These results highlight the potential of the proposed framework to improve the reliability of automatic multi-modal pain recognition and support the development of more objective and inclusive clinical assessment tools.

ABSTRACT Measuring functional status allows clinicians to deliver evidence-based interventions to prevent or delay associated adverse outcomes. Functional status is seldom routinely measured in primary care settings where most older adults receive care. Interprofessional team-based care is increasingly regarded as an important feature of high quality and efficient health care systems. Yet despite growing evidence of the benefits of team-based care, in primary care there are not yet standards for how to operationalize interprofessional practice. In this study we explored interprofessional perspectives on assessing functional status among older adults in team-based VA primary care clinics. We conducted qualitative interviews with 57 primary care team members (nursing staff, primary care providers, and social workers) from six geographically diverse VA medical centers. We drew from implementation science frameworks and sociotechnical theories to ground our thematic analysis in dynamic, real-world contexts. Interviews revealed the view that all primary care team members play a role in measuring and addressing functional status. Participants also described a perceived hierarchy of accuracy of assessment based on role and outlined strategies for validating the accuracy of functional status assessments. These results can inform guidelines for functional status measurement in primary care that improve interprofessional assessment and team-based communication.

Medical knowledge is expanding, diseases are becoming more complex, and healthcare challenges are growing—so why is our approach to training doctors decades behind? Dr. Bradley D. Freeman, in his commentary, highlights the inconsistency between the minimum postgraduate training required for licensure and the actual years of training necessary to ensure physician competency 1. He argues that doctors are granted independent licensure in many jurisdictions after just one year of training, despite evidence indicating that full competency in primary care disciplines typically requires at least three years. Similarly, Dr. Janis M. Orlowski reinforces that a one-year internship is outdated and misaligned with modern medical education standards, which emphasise competency-based progression over fixed training durations 2. This global perspective underscores the urgency of reforming Pakistan’s medical training system, which remains rooted in an outdated model that inadequately prepares fresh medical graduates for independent practice. The one-year house job provides basic clinical exposure but lacks the structured training necessary for developing strong clinical decision-making and procedural skills 3. This deficiency compromises patient care, escalates referrals to tertiary hospitals, and overburdens specialized centers while leaving peripheral healthcare facilities underutilized 4. To address these shortcomings, Pakistan must transition to a mandatory three-year residency program before granting independent licensure. Internationally, models like the U.S. residency system have demonstrated that structured multi-year training enhances physician competency, improves patient outcomes, and reduces unnecessary hospital referrals5. A structured residency not only strengthens clinical expertise but also optimizes healthcare resource utilization—an essential consideration in resource-limited settings like Pakistan. It's time for Pakistan to reconsider and reform its postgraduate medical training system to align with global standards. Implementing a structured three-year residency will not only elevate healthcare standards but also equip future physicians with the skills and confidence needed for independent practice. A modernized, competency-driven training approach is not merely an improvement—it is a necessity for building a competent, efficient, and sustainable healthcare system.

Amid rapid urbanization and accelerated population aging, spatial inequality in the distribution of healthcare facilities has become a pressing challenge in Shenyang. The dual problem of overconcentration of high-level medical resources in the urban core and insufficient primary care provision in peripheral areas highlights systemic imbalances in healthcare equity and efficiency. Grounded in the concept of spatial equity, this study integrates multi-source data-including population statistics, facility locations, and transportation networks-using advanced spatial analysis and big data fusion techniques. Through kernel density estimation, bivariate spatial autocorrelation, and service area network analysis, the spatial distribution and accessibility patterns of healthcare facilities across tertiary, secondary, and primary levels are comprehensively evaluated. To quantify spatial inequity, the Gini coefficient is introduced, confirming significant disparities in per capita healthcare resource allocation across administrative units. By combining service coverage modeling and the Location-Allocation (LA) model, the study identifies "healthcare deserts" and proposes a multi-tiered spatial optimization strategy aligned with China's hierarchical diagnosis and treatment system. Simulation results demonstrate a pronounced "central concentration-peripheral scarcity" pattern, with particularly acute deficiencies in districts such as Shenbei and Hunnan. The planning intervention recommends the addition of six tertiary and six secondary/primary hospitals, along with the spatial reconfiguration of 260 community health service stations, increasing the overall population coverage rate to 98.98%. This research offers empirical evidence and a transferable planning framework for improving healthcare spatial equity through a "core decongestion-periphery reinforcement" approach. It also highlights the role of policy-guided developer participation and collaborative governance in enhancing service provision in newly urbanized areas. The study contributes practical insights for building an accessible, efficient, and resilient multi-level healthcare system, supporting the goals of the "Healthy Shenyang" initiative and offering a replicable model for similar urban contexts.

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing direct risks to patients. However, their integration into healthcare requires careful consideration of ethical, regulatory, and safety constraints in light of the sensitivity and nonlinear nature of human data. In this review, we examine recent progress in DTs over the past seven years and explore broader trends in AI-augmented DTs, focusing particularly on movement rehabilitation. Our goal is to provide a comprehensive understanding of how DTs bolstered by AI can transform healthcare delivery, medical research, and personalized care. We discuss implementation challenges such as data privacy, clinical validation, and scalability along with opportunities for more efficient, safe, and patient-centered healthcare systems. By addressing these issues, this review highlights key insights and directions for future research to guide the proactive and ethical adoption of DTs in healthcare.

Blood is a red-colored fluid in the human body that plays a crucial role in maintaining the immune system. According to the ABO system, blood is classified into four main types: A, AB, B, and O. This classification is essential for facilitating blood transfusions. Currently, blood type determination is still performed manually by healthcare professionals, who observe the presence or absence of clumping (agglutination) in the blood when it reacts with specific antigens.Numerous studies have been conducted to support and enhance healthcare services, particularly as technological advancements continue to grow rapidly across various fields. In the medical field, these advancements have led to the development of increasingly sophisticated medical devices, including blood type detection tools. These devices typically use manual optical sensors to read blood agglutination by detecting changes in light intensity. However, such devices are not fully automated and still require human intervention, making them prone to human error. Today, automated blood type detection systems utilizing cameras and smartphones—integrated with various image processing methods and Artificial Intelligence (AI) are being increasingly developed. Therefore, this study focuses on the development of a blood type detection model that combines image processing and Deep Learning (DL) to support an intelligent, fast, and efficient healthcare system, achieving a detection accuracy of 98%.

Adverse drug reactions (ADRs) and drug toxicity are serious problems in healthcare, threatening patient safety and driving up costs. Though they're not always as immediately obvious as infectious diseases, their consequences can be severe. Detecting these issues early is vital for understanding how safe and effective a drug truly is. Artificial intelligence (AI) and machine learning (ML) are revolutionizing this early detection. These technologies can quickly and accurately predict potential ADRs and toxicity risks long before a drug is even synthesized or enters preclinical and clinical testing. This review explores how AI and ML are used for this purpose, covering a wide range of methods from data mining to deep learning. We dive into the relevant databases, modeling algorithms, and software tools used for ADR and toxicity prediction. By highlighting what these technologies can do, we show their power to fundamentally change drug discovery and make treatments safer for patients. But AI's impact doesn't stop there. This review also looks at how AI is transforming ongoing drug monitoring in healthcare. By enabling real-time data analysis and continuous surveillance, AI helps improve how well drugs work and reduces harmful reactions. Its sophisticated algorithms can make sense of complex patient data, paving the way for personalized treatment plans and precision medicine. Furthermore, AI-driven monitoring systems help lower risks, minimize errors, and optimize patient care, leading to better health results. We also look ahead to AI's future in drug monitoring, considering important ethical and regulatory questions. Ultimately, AI is key to building a more efficient, personalized, and patient-focused healthcare system, promising to reshape how care is delivered and improve outcomes.

Noncommunicable and communicable diseases represent significant public health problems, heavily straining healthcare systems. The care pathway (CP) concept has emerged as a promising framework to improve care coordination and delivery, but its complexity often hinders implementation. Modeling, with its various methodologies, represents a valuable approach to address this challenge. Systematizing these methodologies is essential for enhancing CPs. This scoping review aims to describe and analyze CP modeling methodologies. Scoping review. Following PRISMA-ScR guidelines, searches were performed in PubMed, Web of science and Embase. Inclusion criteria were: (i) publications in English; (ii) human studies, (iii) published between January 1, 2019 and April 3, 2024 and (iv) use of modeling to analyze CPs. For each publication included, data were extracted and categorized based on modeling goals, methods used, functions of the techniques and their respective strengths and limitations. Analysis of the 41 included articles revealed that the main goals of CP modeling were quality of care (46.3%), continuous improvement (31.7%), and process optimization (22.0%). The methods used for modeling were qualitative (41.5%), quantitative (34.1%), or mixed (24.4%). Technical goals were description (48.8%), decision support (36.6%), and prediction (14.6%). Qualitative methods (68.5%) were common in studies focused on quality of care. Only 11 articles shared similar methodologies across at least two studies. Key weaknesses of CP modeling were data availability and implementation acceptance. This scoping review identified key categories and commonly used methodologies in CP modeling, offering a framework to help researchers and healthcare professionals improve CP design and implementation, leading to better patient outcomes and more efficient healthcare systems.

The rapid advancement of digital technologies has significantly impacted healthcare systems worldwide. Digital transformation in healthcare aims to improve patient care, optimize operational efficiency, and enhance decision-making processes. This paper explores the key challenges and opportunities associated with digital transformation in healthcare. While innovations such as artificial intelligence (AI), telemedicine, big data analytics, and blockchain hold great potential, their implementation faces obstacles such as data security concerns, regulatory compliance, resistance to change, and high implementation costs. By analyzing global trends and case studies, this research highlights strategies for overcoming these barriers and leveraging digital transformation to create more efficient, accessible, and patient-centered healthcare systems.