The New Uzbekistan Journal of Medicine (NUJM) is a peer-reviewed scholarly online journal. There is no publication fee in the NUJM for international researchers. The NUJM is an international journal and welcomes research papers in the field of medicine and healthcare, covering diverse topics such as original theoretical works, clinical research, case studies, literature reviews, research reports, public health issues, psychological health, medical education, book reviews, and review articles. Articles submitted to the NUJM should be original, unpublished, and not under consideration for publication elsewhere at the time of submission. Please note that the editor and reviewers dedicate significant time to the review process. Therefore, submissions cannot be withdrawn without approval from the editorial office during the review, revision, production after acceptance, or publication process. All submissions undergo an initial review by the editorial team, which is completed within one week. If the submission passes this stage, a blind peer review process follows, taking approximately two weeks. While using the NUJM article template is not mandatory for submissions under review, accepted papers are encouraged to use the provided template.
EARLY DISEASE DETECTION SYSTEM USING ELECTRONIC MEDICAL RECORDS (EMR)
Early detection of diseases is critical for improving patient outcomes, reducing healthcare costs, and enabling timely interventions. Electronic Medical Records (EMR) store comprehensive patient data, including demographic information, laboratory results, clinical notes, and medication history, providing a valuable resource for predictive healthcare analytics. Leveraging Artificial Intelligence (AI) and machine learning algorithms on EMR data enables the development of early disease detection systems capable of identifying high-risk patients, predicting disease onset, and supporting clinical decision-making. This thesis examines the role of EMR-based AI systems in early disease detection, their methodologies, benefits, and challenges, and evaluates their impact on precision medicine and healthcare efficiency.
Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich
Copyright (c) 2025 Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
PREDICTION OF CHEMOTHERAPY EFFECTIVENESS AND ACCURACY USING ARTIFICIAL INTELLIGENCE
The effectiveness of chemotherapy varies significantly among cancer patients due to genetic, molecular, and environmental factors, making personalized treatment strategies critical for improving outcomes. Traditional methods for predicting chemotherapy response rely on clinical staging, histopathology, and clinician experience, which often fail to account for complex patient-specific variability. Artificial Intelligence (AI) and machine learning (ML) offer powerful tools to analyze multidimensional biomedical data, including genomic profiles, imaging, and clinical records, to predict chemotherapy responsiveness. This thesis examines the role of AI in predicting chemotherapy effectiveness, highlighting algorithmic approaches, data integration strategies, clinical applications, and challenges. By leveraging AI, clinicians can enhance precision oncology, optimize treatment plans, and improve patient survival while minimizing adverse effects.
AUTOMATION OF EPIDEMIOLOGICAL ANALYSIS USING MEDICAL DATABASES AND ARTIFICIAL INTELLIGENCE
Epidemiological analysis is fundamental for understanding disease patterns, identifying risk factors, and guiding public health interventions. Traditional epidemiological research often relies on manual data collection and statistical analysis, which can be time-consuming, error-prone, and limited in scope. The integration of Artificial Intelligence (AI) with large-scale medical databases enables automated, high-precision epidemiological analysis, enhancing disease surveillance, outbreak prediction, and resource allocation. This thesis examines the role of AI-driven automation in epidemiological studies, the methodologies employed, applications in infectious and chronic disease monitoring, and the challenges and future directions of this emerging field. Automated AI approaches offer opportunities to accelerate public health responses, improve data-driven decision-making, and advance predictive epidemiology.
ARTIFICIAL INTELLIGENCE IN TREATMENT OF DIABETES: ENHANCING ACCURACY AND PREDICTIVE OUTCOMES
Diabetes mellitus is a chronic metabolic disorder with rising global prevalence, often complicated by cardiovascular, renal, and neurological conditions. Conventional management relies on intermittent glucose monitoring and standardized therapeutic regimens, which frequently fail to provide personalized, predictive, or timely interventions. Artificial Intelligence (AI), including machine learning and deep learning algorithms, has shown promise in improving diagnosis, monitoring, predictive outcomes, and individualized treatment. This thesis examines the application of AI in diabetes care, highlighting its role in enhancing accuracy, forecasting glycemic trends, and facilitating patient-centered management. Ethical and clinical implementation considerations are also discussed.
MOBILE HEALTH (MHEALTH) APPLICATIONS AND THEIR ROLE IN PATIENT MONITORING AND TREATMENT
The rapid proliferation of smartphones and mobile technologies has led to the emergence of mobile health (mHealth) as a critical component of modern healthcare systems. mHealth refers to the use of mobile devices, applications, and wireless technologies to support medical and public health practices. This study aims to evaluate the role of mHealth applications in patient monitoring, treatment adherence, and overall healthcare delivery. A mixed-methods research approach was employed, integrating quantitative analysis of clinical outcomes with qualitative insights from patients and healthcare professionals. The findings indicate that mHealth applications significantly improve patient engagement, enhance chronic disease management, and facilitate real-time monitoring of health conditions. Additionally, mHealth contributes to improved treatment adherence and reduced hospital readmissions. However, challenges such as data privacy, usability issues, and digital inequality remain significant barriers. The study concludes that mHealth applications have the potential to transform healthcare delivery by promoting personalized, accessible, and patient-centered care.
CYBERSECURITY CHALLENGES IN HEALTHCARE INFORMATION SYSTEMS AND PATIENT DATA PROTECTION
The rapid digitalization of healthcare systems has led to the widespread adoption of electronic health records, telemedicine platforms, and interconnected medical devices, significantly improving healthcare delivery. However, this transformation has also introduced critical cybersecurity challenges, particularly concerning the protection of sensitive patient data. Healthcare information systems have become prime targets for cyberattacks due to the high value of medical data and the complexity of digital infrastructures. This study aims to evaluate the major cybersecurity challenges in healthcare systems, assess their impact on patient data protection, and explore strategies for mitigating associated risks. A mixed-methods approach was employed, combining quantitative analysis of cybersecurity incidents with qualitative insights from healthcare IT professionals. The findings indicate that healthcare systems face increasing threats from ransomware, data breaches, and unauthorized access, which can compromise patient privacy and disrupt clinical operations. Additionally, vulnerabilities related to outdated systems, lack of staff training, and insufficient regulatory compliance contribute to cybersecurity risks. The study concludes that strengthening cybersecurity frameworks, implementing advanced encryption technologies, and enhancing workforce awareness are essential for protecting healthcare data in the digital era.
BIG DATA ANALYTICS IN HEALTHCARE: OPPORTUNITIES FOR DISEASE PREDICTION AND PREVENTION
The rapid growth of digital technologies has led to an unprecedented increase in healthcare data, giving rise to the concept of Big Data in medicine. Big Data analytics refers to the use of advanced computational techniques to process, analyze, and interpret large and complex datasets. In healthcare, these datasets include electronic health records, medical imaging, genomic data, wearable device outputs, and population health statistics. This study aims to evaluate the role of Big Data analytics in disease prediction and prevention, focusing on its impact on healthcare quality, clinical decision-making, and public health outcomes. A mixed-methods approach was employed, combining quantitative analysis of predictive models with qualitative insights from healthcare professionals. The findings demonstrate that Big Data significantly enhances early disease detection, improves risk stratification, and supports personalized medicine. Additionally, Big Data analytics contributes to more efficient healthcare systems by optimizing resource allocation and reducing costs. However, challenges such as data privacy, integration complexity, and algorithm bias remain significant barriers. The study concludes that Big Data analytics has the potential to revolutionize healthcare by enabling predictive, preventive, and personalized medical approaches.
TELEMEDICINE AS A TRANSFORMATIVE TOOL IN MODERN HEALTHCARE SYSTEMS
The rapid advancement of digital technologies has significantly reshaped healthcare delivery systems, with telemedicine emerging as one of the most transformative innovations in modern medicine. Telemedicine enables the remote delivery of healthcare services through information and communication technologies, thereby improving access, efficiency, and quality of care. This study aims to evaluate the impact of telemedicine on healthcare accessibility, clinical efficiency, patient outcomes, and healthcare system performance. A mixed-methods approach was employed, integrating quantitative analysis of clinical indicators with qualitative insights from healthcare professionals and patients. The findings demonstrate that telemedicine significantly enhances healthcare accessibility, reduces waiting times, improves patient satisfaction, and supports continuous monitoring of chronic diseases. Additionally, telemedicine contributes to cost reduction and better resource utilization. However, challenges related to technological infrastructure, data security, regulatory frameworks, and user adaptation remain critical barriers. The study concludes that telemedicine represents a fundamental shift toward more flexible, patient-centered healthcare systems, although further development and integration are required for optimal implementation.
THE IMPACT OF ELECTRONIC HEALTH RECORDS (EHR) ON PATIENT CARE AND CLINICAL DECISION-MAKING
The digital transformation of healthcare has accelerated significantly over the past decades, with Electronic Health Records (EHR) becoming a central component of modern medical practice. EHR systems enable the systematic collection, storage, and sharing of patient information, thereby improving clinical workflows, patient safety, and decision-making processes. This study aims to comprehensively evaluate the impact of EHR systems on patient care quality and clinical decision-making. A mixed-methods approach was employed, combining quantitative performance analysis with qualitative insights from healthcare professionals. The findings demonstrate that EHR implementation significantly improves diagnostic accuracy, reduces medical errors, enhances efficiency, and supports evidence-based decision-making. However, challenges such as interoperability limitations, clinician workload, and cybersecurity risks remain critical barriers. Overall, EHR systems represent a transformative advancement in healthcare, but their effectiveness depends on proper implementation, training, and technological integration.
ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS: ACCURACY, EFFICIENCY, AND CLINICAL IMPACT
Artificial Intelligence (AI) has become one of the most transformative technologies in modern healthcare, particularly in the field of medical diagnostics. The increasing complexity of medical data, combined with the limitations of human cognitive capacity, has created a demand for advanced computational tools capable of improving diagnostic accuracy and efficiency. AI technologies, including machine learning (ML), deep learning (DL), and neural networks, have demonstrated remarkable potential in analyzing large-scale clinical datasets, medical imaging, and electronic health records. This study aims to provide a comprehensive evaluation of the role of AI in medical diagnostics, focusing on its impact on accuracy, efficiency, and clinical outcomes. A mixed-methods approach was used, integrating quantitative analysis of diagnostic performance with qualitative insights from healthcare professionals. The findings reveal that AI significantly enhances diagnostic precision, reduces human error, and improves workflow efficiency. However, challenges such as algorithm bias, lack of explainability, and ethical concerns remain critical barriers. Overall, AI is positioned to revolutionize medical diagnostics, but its integration requires careful regulatory and clinical validation.
THE ROLE OF E-HEALTH SYSTEMS IN IMPROVING HEALTHCARE QUALITY AND ACCESSIBILITY
In recent decades, the integration of information technologies into healthcare systems has significantly transformed the delivery of medical services worldwide. E-health, which encompasses a wide range of digital tools including electronic health records (EHR), telemedicine, mobile health (mHealth), and health information systems, has emerged as a crucial factor in enhancing healthcare quality and accessibility. This study aims to comprehensively evaluate the role of e-health systems in improving clinical outcomes, operational efficiency, and patient satisfaction. A mixed-methods research design was employed, combining quantitative data analysis with qualitative assessments derived from healthcare institutions implementing digital systems. The findings demonstrate that e-health technologies significantly improve diagnostic accuracy, reduce medical errors, and enhance access to healthcare services, particularly in remote and underserved regions. Moreover, the implementation of digital platforms contributes to faster clinical decision-making and improved coordination among healthcare providers. However, challenges such as data security risks, infrastructure limitations, and resistance to technological adoption remain key obstacles. Overall, e-health systems represent a fundamental shift toward more efficient, patient-centered, and accessible healthcare delivery, although continuous technological, regulatory, and organizational improvements are required to maximize their potential.
AI-BASED SELECTION OF PERSONALIZED CANCER THERAPY
Cancer therapy has evolved from standard, one-size-fits-all regimens to personalized approaches tailored to individual patient profiles. Despite advances in genomics and precision medicine, selecting the optimal therapy remains challenging due to tumor heterogeneity, complex molecular interactions, and patient-specific factors. Artificial Intelligence (AI) provides a transformative solution by integrating multi-dimensional biomedical data—including genomics, transcriptomics, proteomics, imaging, and clinical history—to guide personalized treatment selection. This thesis examines AI-based systems for cancer therapy selection, highlighting computational methodologies, clinical applications, advantages, challenges, and future directions. AI-driven approaches have demonstrated improved accuracy in therapy prediction, enhanced patient outcomes, and reduced treatment-related toxicity, representing a paradigm shift in precision oncology.
REMOTE PATIENT MONITORING SYSTEMS USING AI-BASED MOBILE APPLICATIONS
Remote Patient Monitoring (RPM) systems leveraging AI-based mobile applications are transforming healthcare by enabling real-time, continuous patient monitoring outside traditional clinical settings. These systems integrate wearable sensors, mobile platforms, and cloud-based analytics with artificial intelligence to process vast physiological and behavioral datasets, providing predictive insights, early warning alerts, and personalized recommendations. This thesis examines AI-enhanced RPM, emphasizing computational frameworks, clinical applications, advantages, challenges, and future directions. By automating data interpretation and supporting proactive interventions, AI-driven mobile RPM systems enhance patient outcomes, reduce hospitalizations, and promote patient-centered care.
AI-ASSISTED INDIVIDUALIZED ANESTHESIA DOSAGE SELECTION
The administration of anesthesia requires precise dosing to balance patient safety and procedural efficacy. Traditional methods often rely on standardized protocols and clinician experience, which may not account for inter-individual variability in pharmacokinetics and pharmacodynamics. Artificial Intelligence (AI) offers a transformative approach by integrating patient-specific data—such as age, weight, comorbidities, genetic profiles, and real-time physiological parameters—to optimize anesthesia dosing. AI-assisted systems utilize machine learning models and predictive algorithms to recommend individualized doses, reduce adverse events, and enhance recovery outcomes. This thesis explores the application of AI in anesthesia dosage selection, discussing computational frameworks, clinical utility, benefits, limitations, and future directions.
ROLE OF ARTIFICIAL INTELLIGENCE IN AUTOMATIC ANALYSIS OF MEDICAL IMAGES: MRI, CT, X-RAY
Medical imaging plays a pivotal role in diagnosis, treatment planning, and disease monitoring across a wide spectrum of clinical conditions. Conventional interpretation of MRI, CT, and X-ray images relies heavily on radiologist expertise, which is time-consuming, subject to human error, and influenced by inter-observer variability. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL) approaches, has emerged as a transformative tool for automatic analysis of medical images. This thesis examines the role of AI in enhancing diagnostic accuracy, efficiency, and predictive insights in MRI, CT, and X-ray imaging. Applications include automated detection of tumors, fractures, vascular abnormalities, and degenerative conditions. Challenges such as data privacy, algorithmic bias, and integration into clinical workflows are discussed, alongside future directions for AI-driven imaging. AI-driven analysis promises to optimize radiological workflows, reduce diagnostic errors, and improve patient-centered care.
The Impact of Artificial Intelligence-Driven Predictive Models on Public Health Policy Planning and Epidemic Outbreak Management
Artificial Intelligence (AI)-driven predictive models are increasingly being utilized in public health to support policy planning and epidemic outbreak management. This study evaluates the effectiveness of AI-based predictive systems in forecasting disease outbreaks, optimizing resource allocation, and supporting decision-making processes in public health systems. A convergent mixed-methods approach was employed, combining quantitative data from 190 public health professionals, epidemiologists, and data scientists with qualitative insights from case studies and expert interviews. The findings demonstrate that AI-driven models significantly improve outbreak prediction accuracy, enhance response efficiency, and support proactive policy development. However, challenges such as data reliability, ethical concerns, and model transparency remain critical barriers. The study provides strategic recommendations for integrating AI into public health systems.
Data Mining Techniques in Healthcare: Discovering Hidden Patterns for Disease Prevention, Treatment Optimization, and Medical Research Advancement
Data mining techniques have become essential tools in modern healthcare for extracting meaningful insights from large and complex datasets. This study evaluates the role of data mining in discovering hidden patterns that support disease prevention, treatment optimization, and medical research advancement. A convergent mixed-methods approach was employed, combining quantitative data from 185 healthcare professionals and data analysts with qualitative insights from case studies and expert interviews. The findings demonstrate that data mining significantly improves predictive accuracy, enhances clinical decision-making, and accelerates medical research. However, challenges such as data quality, privacy concerns, and algorithmic complexity remain critical barriers. The study provides strategic recommendations for optimizing the use of data mining techniques in healthcare systems.
The Role of Robotics and Intelligent Systems in Surgical Procedures and Their Impact on Precision, Safety, and Recovery Time
Robotics and intelligent systems have significantly transformed surgical procedures by enhancing precision, improving patient safety, and reducing recovery time. This study evaluates the impact of robotic-assisted surgery and intelligent systems on clinical outcomes, surgical efficiency, and postoperative recovery. A convergent mixed-methods approach was employed, combining quantitative data from 182 surgeons and healthcare professionals with qualitative insights from case studies and expert interviews. The findings demonstrate that robotic systems improve surgical accuracy, reduce intraoperative complications, and shorten patient recovery time. However, challenges such as high costs, technical complexity, and training requirements remain barriers to widespread adoption. The study provides strategic recommendations for optimizing the integration of robotic technologies in surgical practice.
The Influence of Natural Language Processing Technologies on Clinical Documentation, Medical Research, and Healthcare Communication Efficiency
Natural Language Processing (NLP) technologies are increasingly transforming healthcare by enabling efficient processing and analysis of unstructured clinical data. This study evaluates the influence of NLP on clinical documentation, medical research, and healthcare communication efficiency. A convergent mixed-methods approach was employed, combining quantitative data from 190 healthcare professionals and researchers with qualitative insights from case studies and expert interviews. The findings indicate that NLP significantly improves documentation accuracy, reduces administrative workload, and enhances research capabilities through automated data extraction and analysis. Additionally, NLP facilitates more efficient communication among healthcare providers. However, challenges such as language ambiguity, data quality, and integration issues remain critical barriers. The study provides strategic recommendations for optimizing NLP implementation in healthcare systems.
Cybersecurity Challenges in Healthcare Information Systems and Strategies for Protecting Sensitive Patient Data in Digital Environments
The rapid digitalization of healthcare systems has significantly increased the volume and complexity of sensitive patient data, making cybersecurity a critical concern. Healthcare Information Systems (HIS) are increasingly targeted by cyber threats, including data breaches, ransomware attacks, and unauthorized access. This study evaluates cybersecurity challenges in healthcare environments and explores strategies for protecting sensitive patient data. A convergent mixed-methods approach was employed, combining quantitative data from 185 healthcare and cybersecurity professionals with qualitative insights from case studies and expert interviews. The findings indicate that while digital systems improve healthcare efficiency, they also introduce significant vulnerabilities. Effective cybersecurity strategies, including encryption, access control, and staff training, are essential for mitigating risks. The study provides comprehensive recommendations for strengthening cybersecurity frameworks in healthcare systems.
Assessing the Role of Wearable Health Technologies and Mobile Health Applications in Personalized Medicine and Patient Engagement
Wearable health technologies and mobile health (mHealth) applications have emerged as key components of personalized medicine and patient-centered healthcare. This study evaluates the role of these technologies in improving patient engagement, health monitoring, and personalized treatment strategies. A convergent mixed-methods approach was employed, combining quantitative data from 180 healthcare professionals and patients with qualitative insights from case studies and expert interviews. The findings indicate that wearable devices and mHealth applications significantly enhance patient engagement, enable real-time health monitoring, and support personalized medical interventions. However, challenges such as data accuracy, privacy concerns, and user adherence remain critical barriers. The study provides recommendations for optimizing the use of wearable and mobile health technologies in modern healthcare systems.
Copyright (c) 2024 Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Use of Clinical Decision Support Systems Based on Artificial Intelligence for Improving Treatment Outcomes and Reducing Medical Errors
Artificial Intelligence (AI)-based Clinical Decision Support Systems (CDSS) are increasingly being integrated into healthcare systems to enhance treatment outcomes and reduce medical errors. This study evaluates the effectiveness of AI-driven CDSS in clinical environments by examining their impact on decision-making accuracy, treatment efficiency, and patient safety. A convergent mixed-methods approach was employed, combining quantitative data from 178 healthcare professionals with qualitative insights from case studies and expert interviews. The findings demonstrate that AI-based CDSS significantly improve treatment accuracy, reduce diagnostic and therapeutic errors, and enhance clinical workflow efficiency. However, challenges such as system integration, user trust, and algorithm transparency remain significant barriers. The study provides recommendations for optimizing the implementation of AI-based CDSS in healthcare systems.
Digital Transformation in Healthcare: The Impact of Cloud Computing Technologies on Medical Data Storage, Processing, and Accessibility
Cloud computing technologies have become a fundamental component of digital transformation in healthcare, enabling scalable data storage, efficient processing, and improved accessibility of medical information. This study evaluates the impact of cloud-based systems on healthcare data management, clinical workflows, and service delivery. A convergent mixed-methods approach was employed, combining quantitative data from 170 healthcare professionals and IT specialists with qualitative insights from case studies and expert interviews. The findings indicate that cloud computing significantly enhances data accessibility, reduces infrastructure costs, and improves operational efficiency. However, concerns related to data security, regulatory compliance, and system reliability remain key challenges. The study provides recommendations for optimizing cloud adoption in healthcare environments.
The Role of Computer Vision and Deep Learning Algorithms in Medical Imaging Analysis and Automated Disease Detection Systems
Computer vision and deep learning algorithms have revolutionized medical imaging analysis and automated disease detection systems by enabling high-precision image interpretation and early diagnosis. This study evaluates the effectiveness of these technologies in improving diagnostic accuracy, reducing human error, and enhancing clinical workflows. A convergent mixed-methods approach was employed, combining quantitative data from 172 healthcare professionals and imaging specialists with qualitative insights from clinical case studies and expert interviews. The findings indicate that deep learning-based systems significantly improve diagnostic performance, particularly in radiology and pathology, while reducing analysis time and variability. However, challenges such as data quality, algorithm transparency, and integration with clinical workflows remain significant. The study provides recommendations for optimizing the use of computer vision technologies in healthcare.
Computer Vision, Deep Learning, Medical Imaging, Disease Detection, Artificial Intelligence
Blockchain Technology in Healthcare: Enhancing Data Security, Privacy Protection, and Interoperability Across Medical Information Systems
Blockchain technology has emerged as a promising solution for addressing critical challenges in healthcare data management, including security, privacy, and interoperability. This study evaluates the role of blockchain in enhancing the protection of sensitive medical data and improving data exchange across healthcare systems. A convergent mixed-methods approach was employed, combining quantitative data from 168 healthcare and IT professionals with qualitative insights from case studies and expert interviews. The findings indicate that blockchain significantly improves data integrity, reduces unauthorized access, and enhances interoperability between medical information systems. However, challenges such as scalability, implementation costs, and regulatory uncertainty remain key barriers. The study provides strategic recommendations for the effective integration of blockchain in healthcare environments.
The Effectiveness of Telemedicine Platforms in Expanding Healthcare Accessibility and Reducing Inequality in Rural and Underserved Populations
Telemedicine platforms have emerged as a critical solution for improving healthcare accessibility, particularly in rural and underserved populations. This study evaluates the effectiveness of telemedicine technologies in reducing healthcare disparities, improving patient outcomes, and enhancing service delivery efficiency. A convergent mixed-methods approach was employed, combining quantitative data from 175 healthcare professionals with qualitative insights from case studies and expert interviews. The findings reveal that telemedicine significantly improves access to healthcare services, reduces travel time and costs, and enhances early diagnosis rates in remote regions. However, challenges such as digital infrastructure limitations, technological literacy, and regulatory barriers remain significant. The study provides strategic recommendations for optimizing telemedicine implementation to ensure equitable healthcare access.
Integration of Internet of Medical Things (IoMT) Technologies in Remote Patient Monitoring and Chronic Disease Management in Digital Healthcare Environments
The rapid development of the Internet of Medical Things (IoMT) has significantly transformed modern healthcare by enabling remote patient monitoring and improving chronic disease management. This study investigates the impact of IoMT technologies on patient outcomes, healthcare efficiency, and clinical decision-making. A convergent mixed-methods approach was employed, combining quantitative data from 165 healthcare professionals with qualitative insights from case studies and expert interviews. The findings indicate that IoMT technologies enhance real-time patient monitoring, improve disease management, and reduce hospital readmission rates. However, challenges related to data security, device interoperability, and technological infrastructure remain significant barriers. The study provides recommendations for optimizing IoMT integration in healthcare systems.
The Application of Big Data Analytics in Predictive Medicine and Its Influence on Early Disease Detection and Preventive Healthcare Strategies
The rapid growth of Big Data analytics has significantly transformed predictive medicine, enabling early disease detection and the development of effective preventive healthcare strategies. This study investigates the impact of Big Data technologies on clinical prediction accuracy, patient outcomes, and healthcare efficiency. A convergent mixed-methods approach was employed, combining quantitative data from 170 healthcare professionals with qualitative insights from case studies and expert interviews. The findings demonstrate that Big Data analytics significantly enhances early diagnosis rates, improves risk prediction models, and supports proactive healthcare interventions. However, challenges such as data integration complexity, privacy concerns, and algorithmic bias remain significant barriers. The study concludes with strategic recommendations for optimizing the use of Big Data in predictive medicine.
Evaluating the Impact of Health Information Systems and Electronic Medical Records on Patient Safety, Data Management, and Healthcare Service Efficiency
Health Information Systems (HIS) and Electronic Medical Records (EMR) have become integral components of modern healthcare infrastructure, significantly influencing patient safety, data management, and service efficiency. This study aims to evaluate the impact of HIS and EMR implementation in clinical settings using a mixed-methods approach. Quantitative data were collected from 160 healthcare professionals, while qualitative insights were obtained from case studies and expert interviews. The results indicate that HIS and EMR systems reduce medical errors, improve data accessibility, and enhance workflow efficiency. However, challenges such as system interoperability, data security concerns, and user resistance remain critical barriers. The study provides strategic recommendations for optimizing the implementation and utilization of HIS and EMR systems in healthcare environments.
The Role of Artificial Intelligence and Machine Learning Technologies in Improving Diagnostic Accuracy and Clinical Decision-Making Processes in Modern Healthcare Systems
Artificial Intelligence (AI) and Machine Learning (ML) technologies are transforming healthcare by enhancing diagnostic accuracy and supporting clinical decision-making processes. This study investigates the effectiveness of AI-driven systems in clinical environments using a comprehensive mixed-methods approach. Quantitative data were collected from 150 healthcare professionals across multiple specialties, while qualitative insights were derived from in-depth clinical case analyses and expert interviews. The findings demonstrate that AI significantly improves diagnostic precision, reduces time-to-diagnosis, and enhances treatment planning. However, concerns regarding data privacy, algorithmic bias, and implementation barriers persist. The study provides strategic recommendations for integrating AI responsibly into healthcare systems.