Healthcare Environment Research Articles

Streamlining Patient Data Processing for Payer Compliance with Scalable Big Data Automation

In the fast-changing environment of value-based healthcare, providers must prepare and submit diagnosis files for patients to payers with ever-changing requirements from CMS for contracts. Changing specifications from payers repeatedly requires a flexible system to automatically process items with little reliance on human intervention. It provides a scalable data engineering framework that makes use of PySpark and Apache Airflow to automate and streamline the generation of patient diagnosis files. Its design allows it to be dynamically adapted to changes in specifications, which enhances both operational efficiency and compliance with the automation of complex data transformation processes and large-scale data processing. This framework not only optimizes the reimbursement process but also strengthens the foundations of value-based care by supporting improved patient outcomes and ensuring financial alignment with payer requirements through improving accuracy, timeliness, and reducing manual coding efforts. The proposed framework significantly enhances data processing speed, achieving a 50% reduction in processing time compared to the traditional ETL approach. It offers scalability improvements, allowing data handling from 1 TB to 5 TB—an increase of four times the previous limit—while minimizing manual effort through automation with Airflow, resulting in a 70% reduction in manual intervention.

A Comprehensive Survey on Requirements and Design of AI-Powered Clinical Intelligence Systems in Healthcare

With the increasing complexity of healthcare services, artificial intelligence (AI) is emerging as a vital tool to support medical practitioners by providing real-time clinical insights, automating routine documentation, and enhancing patient engagement. This survey investigates the design requirements, technical challenges, and ethical considerations for AI-powered clinical intelligence systems intended for healthcare environments. Our study is based on a survey conducted with healthcare professionals, patients, and AI researchers, capturing diverse perspectives on functionality, usability, data privacy, and ethical concerns. This paper synthesizes these insights into a detailed requirement analysis that can guide the development of clinically effective, ethically sound, and user-centric AI systems. Through this analysis, we aim to lay the groundwork for creating AIdriven tools that improve clinical workflows, foster better patient outcomes, and support healthcare professionals in delivering quality care.

The dual roles of empathy in mediating structural empowerment and compassion fatigue among Chinese nurses.

Nurses in dynamic healthcare environments face escalating challenges that impact their efficacy and well-being. These challenges arise from both the increasing complexity of medical procedures and the intense emotional demands of caregiving, often leading to compassion fatigue. A global prevalence of burnout at 30.0% was observed over the past decade. This study examines the relationship between structural empowerment, empathy (cognitive and affective), and compassion fatigue among Chinese nurses. Understanding these relationships is crucial for developing strategies to mitigate compassion fatigue. It also contributes to enhancing nurses' well-being. Conducted at a public hospital in Shanghai, this cross-sectional study surveyed 305 nurses, with 277 valid responses. Structural empowerment was measured using the Conditions for Work Effectiveness Questionnaire-II, empathy with the Kiersma-Chen Empathy Scale, and compassion fatigue with the Compassion Fatigue Short Scale. We performed correlation and mediation analyses to explore the direct and indirect effects of structural empowerment on compassion fatigue through empathy. A significant negative direct effect of structural empowerment on compassion fatigue was evidenced by a coefficient of -0.165 (confidence interval: [-0.311, -0.013]). Further analysis revealed a negative indirect effect through cognitive empathy, indicated by an effect of -0.103 (confidence interval: [-0.220, -0.008]). Increased structural empowerment was linked to higher levels of cognitive empathy, which correlated with decreased compassion fatigue. Conversely, the mediation through affective empathy showed a positive indirect effect, indicated by an effect of 0.126 (confidence interval: [0.047, 0.229]). This suggests that higher structural empowerment was associated with increased affective empathy, leading to higher levels of compassion fatigue. The study illuminates the dual role of empathy within the framework of structural empowerment and its impact on compassion fatigue. Cognitive empathy serves as a protective factor, reducing susceptibility to compassion fatigue, while affective empathy appears to heighten vulnerability. These findings provide direct evidence supporting the dual processing pathways of empathy. It is recommended that healthcare policies integrate organizational support with targeted empathy training, such as cognitive reappraisal, emotion regulation, and peer support, to effectively counteract compassion fatigue among nurses. However, due to the cross-sectional nature and the single-hospital setting, the findings should be interpreted cautiously.

Bridging the continuity: Practice-enhancing publications about the ambulatory care medication-use process in 2022.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. This article identifies, summarizes, and prioritizes literature on the ambulatory care medication-use process (ACMUP) published in 2022 that can describe ambulatory pharmacy practice. The medication-use process (MUP) is the foundational system that provides the framework for safe medication utilization within the healthcare environment and was reimagined to focus on new innovations and advancements in ambulatory pharmacy practice. The ACMUP is defined in this article as having the following components: transitions of care, prescribing, access, dispensing, adherence, and evaluating. Articles evaluating at least one step of the ACMUP were assessed for their usefulness toward practice improvement. A PubMed search for articles published in 2022 was conducted in January 2023 using targeted Medical Subject Headings (MeSH) keywords and the table of contents of selected pharmacy journals, providing a total of 4,125 articles. A thorough review identified 49 potentially practice-enhancing articles: 6 for transitions of care, 9 for prescribing, 5 for access, 2 for dispensing, 6 for adherence, and 21 articles for evaluating. Trends from the impact articles are described. It is important to routinely review the published literature and to incorporate significant findings into daily practice. This article continues a series of articles defining and evaluating the currently published literature around the ACMUP. As healthcare continues to advance and care shifts to ambulatory settings, the ACMUP will continue to be a crucial process to evaluate.

Improving the primary care clinical testing process in southwest Scotland: a systems-based approach

IntroductionAcross all healthcare environments, inadequately specified patient test requests are commonly encountered and can lead to wasted clinician time and healthcare resources, in addition to either missed or unnecessary testing...

The Development of Disinfectant Tolerance in Klebsiella pneumoniae.

Disinfectants are a critical infection control measure that are relied upon globally in a range of settings including healthcare, food production, and domestic environments. However, bacteria have been shown to survive disinfectant treatments when harboured in dry surface biofilms or when disinfectants are used ineffectively. This provides an opportunity for organisms to develop low-level tolerance to various disinfectants. The capability of bacteria to develop adaptations to non-antibiotic antimicrobial agents is often overlooked. This study investigates the ability of Klebsiella pneumoniae NDM-1 strain NCTC 13443 to adapt to a range of common chemical disinfectants (benzalkonium chloride, didecyldimethylammonium chloride, polyhexamethylene biguanide, chlorocresol and bronopol) via serial passage exposure method. After long-term adaptation K. pneumoniae developed tolerance to all tested disinfectants, exhibiting a minimum inhibitory concentration increase of between 30-413% compared to the untreated parent samples. Characterisation of disinfectant cross-tolerance showed that while cross-tolerance can occur, most adapted samples became more susceptible to the second disinfectant treatment, likely because of the fitness cost of adaptation. Observed cross-tolerance/increased susceptibility was not always reciprocated between disinfectant-tolerant samples, indicating that the order of disinfectant exposure is important during tolerance development. This has significant implications for disinfectant cleaning routines, and is likely due to variations in the underpinning tolerance mechanisms, even when the disinfectants display similar mechanisms of action. This study reports the capability and readiness of clinically relevant K. pneumoniae to adapt to common disinfectants that are relied upon every day across the world, delivering much-needed insights into an often-overlooked aspect of antimicrobial resistance.

North Carolina Academic Health Centers and Social Determinants of Health

Background Academic health centers (AHCs) have traditionally focused on 3 core missions: patient care, research, and education. The current changing health care environment requires AHCs to broaden their focus to improve the health of their communities. This study reports the opportunities and challenges for the 5 North Carolina AHCs addressing social determinants of health (SDOH). Methods We used a mixed methods design. We analyzed financial data from systemwide community benefits reports and the Form 990 Schedule H of the primary medical centers (when available) from the 5 AHCs, and we conducted 4 key informant (KI) interviews at each of the 5 AHCs for a total of 20 interviews. Results Overall, AHCs spend a very small percentage of their total spending on community improvement services and community benefit operations. By far, the largest amount spent on community benefit is for treating patients eligible for charity care. Food insecurity and access to health care were the most addressed SDOH. Housing was viewed as a crisis that needed to be addressed at statewide and national levels. Limitations Community benefits reports from North Carolina AHCs lack important details. Key informant interviews provide far more recent information on the quantity and breadth of initiatives than the latest community benefits reports that were all completed pre-pandemic. We only interviewed 4 stakeholders from each AHC in North Carolina. Conclusion A new strategic vision must incorporate social accountability into the AHCs’ core missions to remove barriers and institute and sustain change, but the AHCs face a steep challenge to incorporate social accountability at a core mission level.

Addressing Lean and Safety Readiness Assessment in Territorial Ambulatory Healthcare

Within the international healthcare landscape, it is incumbent upon all organisations to proactively address individual patient needs by offering a wide range of services. In this context, Territorial Ambulatory Healthcare (TAH) is critical to promote equal access to care as the primary point of contact between National Healthcare Systems and individuals. To achieve this goal, the adoption of Lean and Safety Management (L&SM), a managerial approach to proactively reduce clinical risk and process inefficiencies, can support organisations in pursuing high quality care objectives. Despite the positive benefits associated with the adoption of this approach, the feasibility of guaranteeing a sustainable care quality over time may encounter several challenges. Among these, key contributors to failure include the lack of tailored tools and practices suitable for the healthcare environment, insufficient readiness of the context for implementation, and the absence of a comprehensive system-wide performance strategy. Recent studies in literature address these issues presenting theoretical and practical models to assess readiness levels, highlighting opportunities for future developments of these topics. The current empirical study contributes to the existing knowledge by creating a framework to assess the level of readiness for L&SM implementations within TAH. While this context is essential for ensuring high-quality care and equitable access to care services, successfully implementing L&SM initiatives within TAH is challenging due to the organizational complexity and the involvement of multiple, fragmented stakeholders and care providers. Addressing weak Critical Readiness Factors (CRFs) before starting a project can significantly contribute to successfully implement L&SM and sustain improvement over time. This study will be conducted within the TAH setting of a healthcare organisation in Northern Italy and aims 1) to evaluate the specific care setting readiness for L&SM implementations and 2) to develop practical guidelines to support the organisation in increasing its actual readiness level. The theoretical framework will be grasped integrating prior research on Lean readiness assessment, along with specific literature on L&SM sustainability implementation factors within TAH. Once refined and tested with the purpose of developing a practical assessment tool, the framework will be applied within the selected setting, leading to the identification of practical guidelines to facilitate project success and improvements sustainability. Overall, this research will contribute to enrich the knowledge about L&SM implementation within TAH, supporting health organisations, managers, and clinical leadership to sustainably improve care quality, enhancing the role of territorial care to foster social well-being.

Enhancing data management and real‐time decision making with IoT, cloud, and fog computing

AbstractThe convergence of Internet of Things (IoT), Cloud computing, and Fog computing, termed as Interconnected Intelligence (II), has revolutionised data management and real‐time decision‐making across various industries. This study introduces a hybrid architecture that integrates these technologies to optimise resource allocation, reduce latency, and improve decision accuracy. Unlike traditional models that rely heavily on centralised Cloud computing, our approach distributes computational tasks between IoT devices, Fog nodes, and Cloud servers, ensuring efficient real‐time processing closer to the data source. The proposed system demonstrated a 20%–30% reduction in latency compared to Cloud‐only architectures, and a 25% improvement in resource utilisation through dynamic load balancing between Fog and Cloud layers. Additionally, the system showed an increase in decision accuracy by 15%, enhancing real‐time decision‐making capabilities in critical applications such as industrial automation, healthcare, and smart urban environments. Data security and privacy were also significantly improved, achieving a 20% reduction in energy consumption by reducing reliance on centralised Cloud resources. These results were validated using real‐world datasets from industrial, healthcare, and urban environments, underscoring the architecture's capability to support large‐scale IoT deployments. Future research will focus on real‐world validation and the development of enhanced dynamic resource management techniques.

Transforming Healthcare Through Effective Health Administration Practices: A Systematic Review

The role of effective health administration practices is crucial in transforming healthcare organizations by enhancing efficiency, improving patient outcomes, and promoting operational sustainability. This systematic review synthesizes the current body of knowledge on health administration practices that have demonstrated transformative impacts within healthcare settings. A comprehensive literature search across major databases, including PubMed, Scopus, and Google Scholar, was conducted, focusing on studies published in recent years. Key themes emerging from the review include workforce management, patient-centered care, digital health solutions, quality improvement methods, and financial management strategies. Findings suggest that effective health administration practices contribute significantly to optimizing healthcare delivery, reducing costs, and increasing patient satisfaction. However, challenges such as staff resistance and high implementation costs remain barriers to adopting these practices widely. This review highlights the need for continued research into long-term impacts, cost-effectiveness, and adaptability of these practices in diverse healthcare environments. The findings provide valuable insights for healthcare administrators and policymakers aiming to implement evidence-based strategies to foster continuous improvement and adaptability within healthcare systems.

The Impact of Color in Healthcare Environments: A Systematic Review and Research Agenda

: Color plays a significant role in shaping the atmosphere and experience within healthcare environments. This systematic review examines the existing literature on the effects of color in healthcare settings, aiming to provide insights into its influence on patient outcomes, staff well-being, and overall healthcare experiences. By synthesizing findings from various studies, this review identifies key themes, explores methodological approaches, and proposes a comprehensive research agenda to better understand the complex relationship between color and health outcomes. The review involved a systematic search of databases such as PubMed, Scopus, and Web of Science, as well as internet search engines including Google and Yahoo. The search focused on empirical studies, theoretical papers, and industry guidelines related to color in healthcare settings. Over 3 000 titles were screened, and relevant studies were critically evaluated for their methodological rigor and findings. A PRISMA form was used to extract the necessary information. The synthesis of existing literature highlights the importance of considering color selection, application, and environmental context in healthcare design to optimize patient care and enhance the well-being of both patients and healthcare providers. Although the existing literature underscores the importance of color in healthcare design, empirical evidence remains limited. Future research should aim to address these gaps by exploring the nuanced ways in which color influences health outcomes and by developing evidence-based guidelines for the application of color in healthcare settings.

Enhancing Collaborative Leadership in Healthcare: Addressing Ego Conflicts for Organizational Harmony and Improved Patient Care

In today’s complex healthcare environment, fostering collaboration between medical professionals and administrative teams is essential for delivering high- quality patient care. However, non-clinical conflicts, often driven by differences in authority, communication gaps, and ego clashes, can disrupt organizational harmony. These conflicts between doctors, nursing heads, and corporate teams can negatively affect operational efficiency, care outcomes, and staff morale. This article explores the root causes of these conflicts, highlights successful global strategies for resolving them, and provides a comprehensive framework for promoting a culture of mutual respect and teamwork within healthcare settings. By implementing these approaches, healthcare organizations can create a cooperative environment that enhances both staff satisfaction and patient outcomes. Moreover, the article examines how these strategies can be applied globally to address similar challenges across diverse healthcare settings.

Smellscape perceptions in hospital waiting areas: influential factors and semantic dimensions

ABSTRACT Smell can have significant physical and psychological impacts on space users, especially in hospital waiting areas where smells are complex, and users are vulnerable. Understanding how smellscapes affect patients and their companions may help improve space users’ interactions with such environments. This study aimed to identify odour types in hospital waiting areas, their influential factors, impacts on odour perception, and key aspects to characterise and evaluate the smellscape in typical waiting areas. An internet survey and field investigations were conducted in China, and subsequent data were analysed. Results showed that 1) nine primary odours were identified in hospital waiting areas, 2) people have negative but relatively tolerant attitudes towards the odours, 3) several demographic and environmental factors impact people’s odour perception and evaluation, 4) the evaluation of smellscapes is influenced by the overall environmental judgement, with better odour experience reported in smaller and relatively closed waiting areas, and 5) three dimensions – health, emotion, and quality – were identified to characterise the smellscape. This study highlights the importance of smellscape design and management in healthcare environments, considering odour types, individual differences, spatial features, and other senses to systematically improve the smellscape.

Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data

Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as attention-based models and Graph Neural Networks, have made great strides, this work focuses on data augmentation methods that tackle issues like data scarcity and task variability in HAR. In this work, we investigate and expand the use of mixup and cutout data augmentation methods to sensor-based and skeleton-based HAR datasets. These methods were first widely used in Computer Vision and Natural Language Processing. We use both augmentation techniques, customized for time-series and skeletal data, to improve the robustness and performance of HAR models by diversifying the data and overcoming the drawbacks of having limited training data. Specifically, we customize mixup data augmentation for sensor-based datasets and cutout data augmentation for skeleton-based datasets with the goal of improving model accuracy without adding more data. Our results show that using mixup and cutout techniques improves the accuracy and generalization of activity recognition models on both sensor-based and skeleton-based human activity datasets. This work showcases the potential of data augmentation techniques on transformers and Graph Neural Networks by offering a novel method for enhancing time series and skeletal HAR tasks.

Readiness for professional practice among health professions education graduates: a systematic review

Readiness to practice is the state of being prepared and capable of engaging in professional activities in a specific field. Graduates of healthcare professions require a diverse set of skills, knowledge and attitudes to meet the demands of complex healthcare settings. This systematic review provides a comprehensive analysis of readiness for professional practice among graduates of health professions education. It encompasses a wide range of articles published between 2013 and 2024, incorporating various health professions and utilizing a combination of quantitative, qualitative, and mixed methods designs. The review identified 93 studies from 32 diverse countries. The review revealed that factors influencing readiness to practice, include individual capabilities, the workplace context, and educational provision. It also explored strategies to enhance readiness to practice. The findings underscore the significance of addressing challenges such as lack of confidence, stress, communication, time management, job satisfaction, clinical experience, academic workload, teaching quality, mentorship and curriculum design. This comprehensive analysis serves as a valuable resource for healthcare educators, policymakers, and practitioners seeking to optimize the preparedness of graduates for the complexities of contemporary healthcare environments. Future studies should explore the interactions between readiness to practice stakeholders’ perception of the educational curriculum, and the quality of support in the professional practice environment.

Efficient Brain Cancer Classification Based on Advanced CNN and Transformer Technologies

Abstract. This study addresses the challenge faced by primary medical staff in accurately distinguishing different types of brain cancer by comparing the training accuracy and iteration speed of various classification models. Specifically, thesis evaluated a basic Convolutional Neural Network (CNN) alongside two transfer learning models: EfficientNetV2 and Vision Transformer. The analysis is based on publicly available data from Kaggle, which provided a robust dataset for evaluation. The results indicate that both EfficientNetV2 and Vision Transformer models achieve significantly higher iteration convergence speeds and classification accuracy compared to the basic CNN model trained solely on the database. These advanced models demonstrate a capacity for efficient and rapid brain cancer differentiation, making them suitable for settings with limited computing resources. The study highlights that the transfer learning models can effectively support outpatient doctors by providing fast and accurate etiological screening without requiring extensive hardware. Overall, the findings suggest that EfficientNetV2 and Vision Transformer models offer a practical solution for improving brain cancer classification in primary healthcare environments, where rapid and precise diagnosis is crucial. This research underscores the potential of transfer learning techniques to enhance diagnostic capabilities while accommodating resource constraints.

Application of detailed nursing management intervention in neurosurgical nursing.

In the contemporary healthcare environment, optimizing patient care strategies is of importance, particularly in neurosurgical environments. While traditional nursing methods have the foundation of patient care, there exists a growing recognition of the potential benefits of comprehensive nurse management. Despite this acknowledgment, there remains a gap in understanding the comparative effectiveness of comprehensive nurse management versus traditional approaches in reducing postoperative psychological stress, enhancing patient satisfaction, and promoting adherence in neurosurgical patients. This study compares the efficacy of comprehensive nurse management against traditional methods in facilitating the postoperative recovery of neurosurgical patients. Taking the traditional nursing management and detailed nursing management of neurosurgical patients in a municipal neurosurgical hospital from March 2021 to March 2022 as an example, the neurosurgery was divided into 50 patients in the detailed nursing management group and 50 patients in the traditional nursing management group. In the clear nursing management group, there were 50 patients, 20 male patients, with an average age of 58.7 ± 3.8 years, and 30 female patients, with an average age of 60.4 ± 4.3 years; In the traditional nursing management group, there were 50 patients, 26 male patients, with an average age of 59.7 ± 3.7 years, and 24 female patients, with an average age of 59.4 ± 3.9 years; Among the 100 patients, there were 60 cases of cerebral infarction, 25 cases of intracerebral hemorrhage and 15 cases of other neurosurgical diseases. Hundred neurosurgical patients were divided into two groups: comprehensive nursing management group and traditional nursing management group. The results showed that the comprehensive treatment effect of patients with detailed nursing management was better than that of the routine nursing group. At the same time, the complications were reduced, and the patient's satisfaction was higher. Through retrospective analysis and investigation of patients, this paper discusses the clinical application of a detailed nursing management model in neurosurgery, which can reduce the probability of infection and other complications and improve the quality of life and treatment effect of patients.

Economic Evaluations of Health Care Interventions in Pediatric Hospital Care.

The hospital medicine movement thrives in a health care environment committed to providing high-quality, safe, and value-based care. Hospitalists and hospitals continually grapple with many decisions regarding adopting new interventions and deadopting established ones. These decisions span the gamut from tests, treatments, and supportive care, to care models. Traditionally, the choice to adopt one intervention over another is commonly thought of in terms of its direct impact on patient outcomes, benefits, and harms. However, the evolving landscape of health care, characterized by increasing constraints on resources necessitates a broader perspective, one that includes a thorough consideration of the economic implications. The goal is not to minimize costs but rather to maximize value, outcomes achieved for money spent. Economic evaluations of health care interventions can provide this information by quantifying value and assisting health care providers, hospitals, and health systems in deciding which intervention to adopt. Economic evaluations deal with both inputs (ie, costs) and outputs (ie, consequences). Few economic evaluations in pediatric hospital medicine have been published, and many clinicians are unfamiliar with them. This paper discusses the economic evaluation of health care interventions with special attention to the pediatric hospitalist and hospital care. The paper aims to give readers an understanding of the key concepts underlying economic evaluations.

Enhancing Coronary Revascularization Decisions: The Promising Role of Large Language Models as a Decision-Support Tool for Multidisciplinary Heart Team.

While clinical practice guidelines advocate for multidisciplinary heart team (MDHT) discussions in coronary revascularization, variability in implementation across health care settings remains a challenge. This variability could potentially be addressed by language learning models like ChatGPT, offering decision-making support in diverse health care environments. Our study aims to critically evaluate the concordance between recommendations made by MDHT and those generated by language learning models in coronary revascularization decision-making. From March 2023 to July 2023, consecutive coronary angiography cases (n=86) that were referred for revascularization (either percutaneous or surgical) were analyzed using both ChatGPT-3.5 and ChatGPT-4. Case presentation formats included demographics, medical background, detailed description of angiographic findings, and SYNTAX score (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery; I and II), which were presented in 3 different formats. The recommendations of the models were compared with those of an MDHT. ChatGPT-4 showed high concordance with decisions made by the MDHT (accuracy 0.82, sensitivity 0.8, specificity 0.83, and kappa 0.59), while ChatGPT-3.5 (0.67, 0.27, 0.84, and 0.12, respectively) showed lower concordance. Entropy and Fleiss kappa of ChatGPT-4 were 0.09 and 0.9, respectively, indicating high reliability and repeatability. The best correlation between ChatGPT-4 and MDHT was achieved when clinical cases were presented in a detailed context. Specific subgroups of patients yielded high accuracy (>0.9) of ChatGPT-4, including those with left main disease, 3 vessel disease, and diabetic patients. The present study demonstrates that advanced language learning models like ChatGPT-4 may be able to predict clinical recommendations for coronary artery disease revascularization with reasonable accuracy, especially in specific patient groups, underscoring their potential role as a supportive tool in clinical decision-making.

Assessment of the Sokolow Lyons Criteria of Left Ventricular Hypertrophy in Comparison with Echocardiography in Hypertensive Patients

Around 65.4% of people over the age of 60 have hypertension, and it's the underlying cause of 6% of all fatalities globally. The left ventricle enlarges, or hypertrophies, in people with hypertension. If the left ventricle of the heart thickens its walls or the chamber of the left ventricle expands, or if both happen at once, the condition is called left ventricular hypertrophy (LVH) [1]. This condition impacts approximately 15 -20% of the general population and has similar rates in men and women. Serious health complications such as cardiovascular diseases, stroke, congestive heart failure, irregular heart rhythms (ventricular arrhythmias), and sudden cardiac death are linked to it.Rationale of the study: In people with slightly hypertensive conditions, the prevalence of LVH is 20%; in those with severe or complex hypertension, it approaches 100%. LVH is also a result of valve disorders, which cause pressure and/or volume overload. Furthermore, "physiological" LVH—which occurs in well-trained athletes in reaction to job overload—can arise. About 0.2% of the general population has hypertrophic cardiomyopathy, a hereditary condition caused by mutations in the sarcomere protein genes. It is characterized by severe cardiac tissue disarray, fibrotic scarring, and aberrant internal coronary arteries.Background and statement of the problem: Hypertension is a leading cause of many cardiovascular problems and cerebrovascular accidents Aim: Assessment of the Sokolowlyon’s Criteria of Left Ventricular Hypertrophy in comparison with Echocardiography Inhypertensives PatientsObjective: To determine whether the ECHO of left ventricular hypertrophy significantly coincides with the Sokolow Lyon criterion of the ECG.Methodology: This cross-sectional study was carried out at the Shree Balaji Medical College and Hospital's general medicine department in Chromepet, Tamil Nadu, India. The study ran from June 2022 to December 2023, a duration of eighteen months.Results: The study focused on assessing the correlation between Sokolow-Lyon criteria on (ECG) and (LVH) as determined by echocardiography (ECHO). The results revealed that among the 125 participants, 88 cases (70.40%) were identified as having LVH based on Sokolow-Lyon criteria, while 37 cases (29.60%) showed no signs of LVH. These findings provide a quantitative breakdown of the prevalence of LVH according to ECG criteria within the study population.Conclusion: Our investigation's findings showed that the ECG LVH Sokolow Lyon criteria had a satisfactory level of specificity and sensitivity in the diagnosis of left ventricular hypertrophy (LVH). The most sensitive diagnostic criteria turned out to be the ECG LVH Sokolow Lyon criteria, especially when it came to patients with co-morbidities. We found that the Sokolow Lyon criteria had a 36% sensitivity in the current investigation. As such, we suggest applying these criteria to assist in verifying the diagnosis of LVH, particularly in low-complexity healthcare environments without image-based diagnostic techniques like MRI and ECHO.