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Journal articles on the topic 'Healthcare Analytics Platform'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Suman Etikala, Suman Etikala. "Healthcare Analytics Platform: Engineering the Future of Data-Driven Healthcare." International Journal of Advances in Engineering and Management 7, no. 4 (2025): 167–73. https://doi.org/10.35629/5252-0704167173.

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This article examines the implementation of an advanced healthcare analytics platform that transformed patient care delivery and operational efficiency across a major healthcare network. The solution addresses critical challenges in data integration, regulatory compliance, and operational demands while demonstrating the effectiveness of a structured three-phase implementation approach. The platform successfully integrates diverse healthcare data sources including Electronic Health Records, clinical systems, IoT devices, and external databases while maintaining strict HIPAA compliance. The impl

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Arjun, R., and Sunil C. D'Souza. "Software Analytics Platform for Converged Healthcare Technologies." Procedia Technology 24 (2016): 1431–35. http://dx.doi.org/10.1016/j.protcy.2016.05.169.

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Štufi, Martin, Boris Bačić, and Leonid Stoimenov. "Big Data Analytics and Processing Platform in Czech Republic Healthcare." Applied Sciences 10, no. 5 (2020): 1705. http://dx.doi.org/10.3390/app10051705.

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Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms,

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Researcher. "ADVANCING CLINICAL DECISION SUPPORT: A TECHNICAL ANALYSIS OF IBM WATSON HEALTH'S AI-DRIVEN HEALTHCARE ANALYTICS PLATFORM." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1265–75. https://doi.org/10.5281/zenodo.14170059.

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This technical article comprehensively examines IBM Watson Health's AI-driven healthcare analytics platform, focusing on its implementation framework and clinical applications. The article explores the platform's advanced analytics capabilities, diagnostic support systems, and specialized oncology applications while providing detailed insights into its technical architecture and deployment methodologies. The article demonstrates how machine learning algorithms, real-time analytics processing, and sophisticated data integration mechanisms work together to create an effective healthcare decision

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López-Martínez, Fernando, Edward Rolando Núñez-Valdez, Vicente García-Díaz, and Zoran Bursac. "A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management." Algorithms 13, no. 4 (2020): 102. http://dx.doi.org/10.3390/a13040102.

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Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population he

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More, Priyanka, Sachin Sakhare, Manya Gupta, Kirti Singh, Om Gavhane, and Rushikesh Mantri. "Predictive Analytics in Healthcare: Empowering Consultation with Machine Learning." International Journal on Recent and Innovation Trends in Computing and Communication 12, no. 1 (2023): 69–76. http://dx.doi.org/10.17762/ijritcc.v12i1.7912.

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The Smart Healthcare and Online Consultation initiative intends to offer patients a quick and convenient online platform for seeking medical advice and services. Real-time video consultations, appointment scheduling, prescription administration, and health records management are just a few of the capabilities available on the platform. To deliver individualized and superior healthcare services, the initiative to use cutting-edge such as AI, ML, and data analytics. By giving patients an easy and affordable way to receive healthcare services remotely, the Smart Healthcare and Online Consultation

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Asri, Hiba, Hajar Mousannif, and Hassan Al Moatassime. "Big Data Analytics in Healthcare." International Journal of Distributed Systems and Technologies 10, no. 4 (2019): 45–58. http://dx.doi.org/10.4018/ijdst.2019100104.

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Sensors and mobile phones shine in the Big Data area due to their capabilities to retrieve a huge amount of real-time data; which was not possible previously. In the specific field of healthcare, we can now collect data related to human behavior and lifestyle for better understanding. This pushed us to benefit from such technologies for early miscarriage prediction. This research study proposes to combine the use of Big Data analytics and data mining models applied to smartphones real-time generated data. A K-means data mining algorithm is used for clustering the dataset and results are transm

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Babu, Gokulnath Chandra, and S. P. Shantharajah. "Survey on data analytics techniques in healthcare using IOT platform." International Journal of Reasoning-based Intelligent Systems 10, no. 3/4 (2018): 183. http://dx.doi.org/10.1504/ijris.2018.096197.

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Babu, Gokulnath Chandra, and S. P. Shantharajah. "Survey on data analytics techniques in healthcare using IOT platform." International Journal of Reasoning-based Intelligent Systems 10, no. 3-4 (2018): 183. http://dx.doi.org/10.1504/ijris.2018.10017495.

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Suzuki, K. "New platform of data analytics for mental health." European Psychiatry 33, S1 (2016): S33. http://dx.doi.org/10.1016/j.eurpsy.2016.01.863.

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IntroductionMental disorder is a key public health challenge and a leading cause of disability-adjusted life years (DALYs) due to its high level of disability and mortality. Therefore, a slight improvement on mental care provision and management could generate solid benefits on relieving the social burden of mental diseases.ObjectivesThis paper presents a long-term vision of strategic collaboration between Fujitsu Laboratories, Fujitsu Spain, and Hospital Clinico San Carlos to generate value through predictive and preventive medicine improving healthcare outcomes for every clinical area, benef

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Ahmed, Zeeshan, Minjung Kim, and Bruce T. Liang. "MAV-clic: management, analysis, and visualization of clinical data." JAMIA Open 2, no. 1 (2018): 23–28. http://dx.doi.org/10.1093/jamiaopen/ooy052.

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AbstractObjectivesDevelop a multifunctional analytics platform for efficient management and analysis of healthcare data.Materials and MethodsManagement, Analysis, and Visualization of Clinical Data (MAV-clic) is a Health Insurance Portability and Accountability Act of 1996 (HIPAA)-compliant framework based on the Butterfly Model. MAV-clic extracts, cleanses, and encrypts data then restructures and aggregates data in a deidentified format. A graphical user interface allows query, analysis, and visualization of clinical data.ResultsMAV-clic manages healthcare data for over 800 000 subjects at UC

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Sriphalya, Akula Veera Vasu, Geetha Usha Sri Bade, Sree Varenya J, and Manas Kumar Yogi. "Health Guard 1.0: Next-Generation Chronic Condition Management Platform." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 45 (August 10, 2024): 41–55. http://dx.doi.org/10.55529/jaimlnn.45.41.55.

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Health Guard 1.0 represents a cutting-edge platform designed to revolutionize the management of chronic conditions. Leveraging advanced technologies such as artificial intelligence, machine learning, and big data analytics, health Guard 1.0 offers a comprehensive solution for individuals, healthcare providers, and researchers alike. The platform facilitates personalized care plans tailored to each patient's unique needs, empowering them to actively participate in their health journey. Through continuous monitoring, predictive analytics, and real-time feedback, Health Guard 1.0 enables early de

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Kumari L, Kavana. "E-Blood Bank System (Referral of Active Blood Donors and Analysis of Donors)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem28488.

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The E-Blood Bank System is a comprehensive online platform aimed at optimizing blood donation processes by prioritizing the referral of active blood donors and conducting thorough analyses of donor data. This system focuses on improving the efficiency of blood donation networks to ensure a consistent and sustainable blood supply for healthcare institutions. Its key feature involves a user-friendly interface allowing potential donors to register and create profiles, providing essential information on blood type, availability, and willingness to donate. Intelligent algorithms match donor profile

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Ifeanyi Ibeh, Augustine, Olusegun Bamidele Oso, Oluwaseyi Inumidun Alli, and Abdulraheem Olaide Babarinde. "Scaling Healthcare Startups in Emerging Markets: A Platform Strategy for Growth and Impact." International Journal of Advanced Multidisciplinary Research and Studies 5, no. 1 (2025): 838–54. https://doi.org/10.62225/2583049x.2025.5.1.3729.

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Scaling healthcare startups in emerging markets is pivotal for addressing critical healthcare gaps and improving access to quality care. However, these startups face significant challenges, including operational inefficiencies, limited access to funding, and barriers to regional expansion. This study introduces a platform strategy model designed to overcome these challenges and drive sustainable growth and impact. The proposed model leverages digital platforms, strategic partnerships, and data-driven decision-making to enhance operational efficiency, expand market reach, and attract investment

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Deepa P., Badrinath P., Mohanaprasanth N., Pranesh S., and Sriram E. "AI-Powered Doctolib: Revolutionizing Healthcare Appointment Management." Asian Journal of Basic Science & Research 06, no. 04 (2024): 90–102. https://doi.org/10.38177/ajbsr.2024.6407.

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Healthcare systems face persistent challenges in managing appointments and improving accessibility, particularly in underserved regions. This paper explores the development and implementation of Doctolib, an AI-driven healthcare platform designed to address these issues. By utilizing advanced algorithms, real-time scheduling, and predictive analytics, Doctolib enhances the efficiency of appointment management. Key features include an AI chatbot for 24/7 patient support, secure telemedicine integration for remote consultations, and demand optimization tools to better allocate resources. The pla

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Ray, Rajarshi (Raj), Zack Agar, Pratik Dutta, Streetam Ganguly, Purushottam Sah, and Debarshi Roy. "MenGO: A NOVEL CLOUD-BASED DIGITAL HEALTHCARE PLATFORM FOR ANDROLOGY POWERED BY ARTIFICIAL INTELLIGENCE, DATA SCIENCE & ANALYTICS, BIO- INFORMATICS AND BLOCKCHAIN." Biomedical Sciences Instrumentation 57, no. 4 (2021): 476–85. http://dx.doi.org/10.34107/kszv7781.10476.

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Digital innovation & transformation, a technology revolution triggered by the latest advancements in the IT sector, has redefined several socially significant domains including healthcare, agriculture, food, finance, and education since the turn of this millennium. Although the power of digital technology has played a key role in modernizing many areas of the healthcare arena, a critical sub-category like andrology i.e., sexual and reproductive health of men, is yet to reap the full benefit of digitalization. This paper describes and explains how MenGO, the world’s 1st data science and ana

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L, Kavana Kumari. "Dare To Donate Application (An E-Blood Bank System)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33916.

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Dare To Donate application is a comprehensive online platform aimed at optimizing blood donation processes by prioritizing the referral of active blood donors and conducting thorough analyses of donor data. This system focuses on improving the efficiency of blood donation networks to ensure a consistent and sustainable blood supply for healthcare institutions. Its key feature involves a user-friendly interface allowing potential donors to register and create profiles, providing essential information on blood type, availability, and willingness to donate. Intelligent algorithms match donor prof

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Kolte, Prof Roshan. "Medicare: Doctor Appointment Website and Hosting Using Cloud Services." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33795.

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The "Doctor Appointment Website Hosting with Cloud Services" project aims to revolutionize the healthcare appointment scheduling process by leveraging modern web technologies and cloud infrastructure. The web application provides a user-friendly platform for patients and healthcare providers to efficiently manage appointments, access relevant information, and communicate seamlessly. By integrating AWS cloud services, the platform ensures scalability, reliability, and accessibility, accommodating growing user bases and maintaining high availability. Additionally, the project incorporates teleme

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Sindhu, C. S., and Nagaratna P. Hegde. "A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2798. http://dx.doi.org/10.11591/ijece.v7i5.pp2798-2805.

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With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an error-free data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in o

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C., S. Sindhu, and P. Hegde Nagaratna. "A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2798–805. https://doi.org/10.11591/ijece.v7i5.pp2798-2805.

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With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in on

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Shaikh, Asma. "Enhancing Medical Awareness through Digital Engagement: A Case Study on the HidocDr. Platform." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1377–78. https://doi.org/10.22214/ijraset.2025.66542.

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In the evolving healthcare landscape, digital platforms have become essential tools for continuous medical education, offering healthcare professionals (HCPs) easy access to the latest clinical updates. The HidocDr. platform exemplifies this transformation by leveraging advanced communication strategies to engage HCPs across various specialties. This study explores the effectiveness of a multi-channel outreach campaign aimed at enhancing HCP awareness and engagement through personalized digital content. By using mobile applications, websites, email, SMS, and WhatsApp, the campaign shared evide

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Manoj Varma Lakhamraju. "Enhancing compensation administration in healthcare: A Workday ERP Perspective." International Journal of Science and Research Archive 12, no. 2 (2024): 3055–64. https://doi.org/10.30574/ijsra.2024.12.2.1147.

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Effective compensation management plays a critical role in healthcare organizations, directly impacting employee satisfaction, retention, and the bottom line of patient care. The complexity of healthcare payrolls due to multiple employee responsibilities, changing schedules, compliance, and employee support poses a major challenge for HR leaders. Traditional payroll processes often lack the flexibility and analytical resources needed to address these issues, leading to poor management and employee dissatisfaction. This article examines the transformative potential of Workday ERP, a cloud-based

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Razali, Mohd Norhisham. "Digital Freelancing Trends Based on Crowdsourcing Technology Analysis Using Data Analytics and Visualization Approaches." Information Management and Business Review 17, no. 2(I) (2025): 66–78. https://doi.org/10.22610/imbr.v17i2(j).4419.

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The increasing living costs have put a strain on people's ability to afford necessities like housing, transportation, education, and healthcare, which affects the quality of life in many countries. These factors have led to a rapidly growing trend of digital freelancing activities that utilize crowdsourcing technology to provide people with opportunities to earn additional income. However, a comprehensive understanding of the digital crowdsourcing landscape is hampered by the lack of studies examining it through a geographical lens, freelancer performance metrics, skillsets, and platform infor

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Samuel Jesupelumi Owoade, Abel Uzoka, Joshua Idowu Akerele, and Pascal Ugochukwu Ojukwu. "Innovative cross-platform health applications to improve accessibility in underserved communities." International Journal of Applied Research in Social Sciences 6, no. 11 (2024): 2727–43. http://dx.doi.org/10.51594/ijarss.v6i11.1723.

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The development of innovative cross-platform health applications has the potential to transform healthcare delivery in underserved communities by increasing accessibility, reducing disparities, and improving health outcomes. These applications are designed to operate across multiple devices and operating systems, ensuring that people in marginalized or remote areas with limited technological infrastructure can still access essential healthcare services. This study explores the development and implementation of cross-platform mobile health (mHealth) applications that provide features such as te

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Kaur, Jagreet, and Dr Kulwinder Singh Mann. "AI based HealthCare Platform for Real Time, Predictive and Prescriptive Analytics using Reactive Programming." Journal of Physics: Conference Series 933 (January 3, 2018): 012010. http://dx.doi.org/10.1088/1742-6596/933/1/012010.

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Kamala Kannan Munusamy Ethirajan. "ServiceNow: Boosting Productivity and Innovation in Healthcare, Manufacturing, and Research." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 982–94. https://doi.org/10.32628/cseit251112101.

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ServiceNow has emerged as a transformative force in enterprise digital transformation across healthcare, manufacturing, and research sectors. The platform's multi-instance cloud architecture enables robust workflow automation and system integration capabilities, fundamentally changing how organizations operate and deliver services. In healthcare, ServiceNow optimizes patient care through automated scheduling, EHR integration, and clinical pathway management, while ensuring regulatory compliance. The manufacturing sector benefits from enhanced production workflows, quality control automation, a

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Satish Kumar Nadendla. "Innovating Global Healthcare Solutions with AWS: Utilizing AI, Data Analytics, and Cloud Services for Disease Control and Personalized Medicine." World Journal of Advanced Research and Reviews 14, no. 3 (2022): 801–14. https://doi.org/10.30574/wjarr.2022.14.3.0472.

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With the recent developments in AI, Data analytics, and Cloud computing, the global healthcare sector has seen a drastic transformation with the former offering solutions for disease control and personalized medicine. Amazon Web Services (AWS) is a strong, scalable, and secure platform to combine these technologies and improve healthcare delivery. In this paper, we will discuss how AI and data analytics powered by AWS allow for predictive modeling, early disease detection, and real-time monitoring of patients, leading to positive healthcare outcomes. Their data processing for precision medicin

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Goutham, Bilakanti. "Building a Healthcare Machine Learning Model with Metaflow." International Journal of Leading Research Publication 3, no. 7 (2022): 1–13. https://doi.org/10.5281/zenodo.15196855.

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The Metaflow data science platform to create and deploy a machine learning model for medical use cases. The pipeline proposed allows for effective data ingestion, preprocessing, model training, evaluation, and deployment along with scalability, reproducibility, and versioning. Through Metaflow's workflow management features, the system streamlines data-driven decisions in medicine. The model uses advanced machine learning to generate predictive health outcome analytics for patients, risk assessment, and individualized treatment advice. The model optimizes clinical effectiveness through automat

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P., Sampath, Ellakkiah S., Kavinmathy G M., and Logeshwaran D. "Environmental Air Pollution and Water Quality Systems in Educational Institutions." Journal of Electrical Engineering and Automation 7, no. 2 (2025): 192–203. https://doi.org/10.36548/jeea.2025.2.008.

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An intelligent environmental monitoring system receives real-time conditions through continuous interpretation of several environmental parameters to provide dependable results alongside ongoing updates. The data communication through IoT connectivity creates a smooth transmission to central platforms which enables remote observation along with preventive decision-making to boost health and environmental sustainability. The system operates over extended periods by using automated analytics that track environmental changes with data-driven evaluation methods. It provides an accessible platform

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Subodh, Singh Priya. "ORGANATE." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem03935.

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Abstract - Organate is a modern, integrated healthcare management platform designed to streamline the organ donation process, health data tracking, and electronic medical record management. It addresses key challenges in organ transplantation, including compatibility assessment, fragmented data systems, and inefficient communication. A core feature of the platform is its intelligent donor-recipient matching system, which calculates a match score based on blood group compatibility, urgency level, health data from wearable devices (such as Fitbands), and organ availability. Built with technologi

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RAJAPANDIAN, MRP. "CAREBRIDGE: A SMART NETWORK FOR EFFORTLESS PATIENT DOCTOR INTERACTION AND DATA SHARING." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04488.

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ABSTRACT: With the rapid pace of the digital healthcare transformation, there has never been a greater urgency for secure and reliable communication tools among patients, caregivers, and healthcare providers. CareBridge is an intelligent and integrated healthcare platform that builds on the technology available today to bring healthcare care delivery closer to the expectations of a digital society. CareBridge uses cloud computing, artificial intelligence, Electronic Health Record (EHR) integrations, and real-time communication to enable patients and healthcare providers to schedule appointment

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Researcher. "SECUREHEALTH: A DECENTRALIZED FEDERATED LEARNING FRAMEWORK WITH BLOCKCHAIN INTEGRATION FOR PRIVACY-PRESERVING HEALTHCARE ANALYTICS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 933–44. https://doi.org/10.5281/zenodo.14275204.

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This article presents a novel decentralized artificial intelligence platform that addresses the critical challenges of implementing machine learning in healthcare while maintaining patient privacy and regulatory compliance. The proposed architecture integrates federated learning with blockchain technology to enable healthcare institutions to train AI models locally while preserving sensitive patient data within their premises. The article demonstrates how distributed learning can be achieved without centralizing patient information, while blockchain integration ensures transparent and immutabl

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Harb, Hassan, Hussein Mroue, Ali Mansour, Abbass Nasser, and Eduardo Motta Cruz. "A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications." Sensors 20, no. 7 (2020): 1931. http://dx.doi.org/10.3390/s20071931.

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Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients’ classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper

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Aravind Puppala. "AI-Augmented Business Intelligence in Healthcare Enterprise Systems: Case Studies of Integration for Performance, Outcomes, and Efficiency." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1345–52. https://doi.org/10.30574/wjaets.2025.15.3.1053.

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The integration of artificial intelligence into business intelligence systems is transforming healthcare delivery through enhanced predictive capabilities and decision support. This article presents case studies of successful AI-BI implementations at leading healthcare institutions, demonstrating significant improvements in operational efficiency, clinical outcomes, and financial performance. Mayo Clinic's patient flow optimization system and Cleveland Clinic's clinical risk stratification platform showcase the transformative potential of AI-augmented analytics in healthcare enterprise environ

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Renuka Rajendra Kajale. "VACCIDOC: An Infant Immunization Tracker for Private Hospitals by Integrating OTP- based Verification." Communications on Applied Nonlinear Analysis 32, no. 4s (2024): 183–89. https://doi.org/10.52783/cana.v32.2748.

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Vaccidoc aims to address inefficiencies in infant immunization management by providing a secure, integrated, and user-friendly digital platform tailored for private hospitals. The system leverages OTP-based verification for secure access, scheduling algorithms for optimized appointment booking, and analytics to track vaccination trends and outcomes. It incorporates a responsive frontend for patients, healthcare providers, and administrators; a robust backend for core functionalities such as user management and record-keeping; and a secure centralized database for vaccination data. Additionally

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Ahmed, Sufyaan. "SHERM: System of Electronic Health Record Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40771.

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The rise of non-communicable diseases (NCDs), particularly in India, highlights the need for innovative health- care solutions. SHERM (System of Electronic Health Record Management Software) addresses these challenges by offering a next-generation platform that integrates machine learning (ML), NFC, and QR technologies to enhance healthcare delivery. SHERM combines advanced data collection techniques, predic- tive analytics, and secure, real-time data access to improve patient outcomes and streamline healthcare processes. Its user-friendly design, developed using the MERN stack and Ionic frame

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Vardhan Reddy, G. Vishnu. "Medical Adherence and Reminder System Platform." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47837.

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Abstract : Medbot.AI is an innovative health-tech platform developed to enhance medication adherence and facilitate comprehensive health monitoring. The system addresses the limitations of conventional solutions by integrating real-time health parameter tracking, automated medication reminders, and AI-powered user interaction. The platform employs technologies such as React.js for frontend development, Node.js and Flask for backend microservices, Firebase for data storage, and Twilio for communication. Real-time health data is acquired through Fitbit integration, and personalized assistance is

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Munagandla, Vamshi Bharath, Sandeep Pochu, Sai Rama Krishna Nersu, and Srikanth Reddy Kathram. "Real-Time Data Integration for Emergency Response in Healthcare Systems." Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930) 3, no. 1 (2024): 25–38. https://doi.org/10.60087/japmi.vol.03.issue.01.id.002.

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The demand for swift and efficient emergency response in healthcare has intensified with the rise of global health crises and increasingly complex healthcare systems. Real-time data integration plays a crucial role in enabling healthcare providers to respond effectively to emergencies by providing a comprehensive, updated view of patient data, resource availability, and situational developments. This paper explores a real-time data integration framework designed specifically for emergency response within healthcare systems, focusing on how integrated data can enhance decision-making, resource

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Kharbouch, Abdelhak, Youssef Naitmalek, Hamza Elkhoukhi, et al. "IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data." International Journal of Distributed Systems and Technologies 10, no. 4 (2019): 17–30. http://dx.doi.org/10.4018/ijdst.2019100102.

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Recent advances in pervasive technologies, such as wireless, ad hoc networks, and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as the Internet of Things (IoT). The IoT is seen as an enabler to the development of intelligent and context-aware services and applications. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data technologies and data analytics have been recently proposed for timely analyzing information (i.e., data, events) streams. Th

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Chintakindhi, Sai Kishore. "Scalable Data Validation Strategies for Big Data and Analytics on Google Cloud Platform (GCP)." International Journal of Multidisciplinary Research and Growth Evaluation 6, no. 2 (2025): 1861–72. https://doi.org/10.54660/.ijmrge.2025.6.2.1861-1872.

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This study dives into the challenge of keeping data in check when handling really large datasets on cloud setups like the Google Cloud Platform (GCP). Researchers looked at heaps of data from all sorts of industries using GCP and ended up suggesting cleaner ways to keep data accurate—a bit like giving a routine check-up to messy information. In most cases, these new methods seem to cut errors down significantly; they bumped data accuracy by roughly 30% while also trimming processing times compared to the old, tired approaches. It’s hard not to notice that in areas like healthcare—where every s

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Ashwini R, Arshika G, Arulkumar R, Chendhi Divya, and Dharanidharan S. "Multiple Disease Detection System Using Biomarkers." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2567–75. https://doi.org/10.32628/cseit251112272.

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The development of a real-time health monitoring system using biomarkers and Machine Learning (ML) algorithms, implemented on an IoT-enabled embedded platform for continuous disease prediction and preventive healthcare. The system integrates non-invasive sensors to track key biomarkers such as Temperature, Humidity, BP_Diastolic, SpO2, BPM and pH, enabling real-time monitoring of physiological parameters. Utilizing the computational capabilities of an edge-processing microcontroller, the collected data is processed using a K-Nearest Neighbor (KNN) algorithm, classifying health conditions based

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Do, Thi-Thu-Trang, Quyet-Thang Huynh, Kyungbaek Kim, and Van-Quyet Nguyen. "A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges." Applied Sciences 15, no. 14 (2025): 8089. https://doi.org/10.3390/app15148089.

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The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edg

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Kumar Sehrawat, Sunil. "Intelligent Healthcare Management: Advancing Healthcare with Integrated AI and ML Solutions." International Journal of Research in Medical Sciences and Technology 16, no. 1 (2023): 115–29. http://dx.doi.org/10.37648/ijrmst.v16i01.016.

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The proposed system, leveraging the power of big data, telecommunication technologies, and wearable sensors, presents a unique opportunity to transform the healthcare industry. It fosters a seamless connection between patients, wearable sensors, caregivers, and providers through the innovative use of Information and Communication Technology (ICT) and software. This is of utmost importance in developing countries, where the healthcare sector grapples with economic challenges amplified by a burgeoning population and a surging demand for quality care, particularly for the elderly. The urgency for

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N G, Suma. "Diagnosis of Acute Diseases Using AI (RogiDoot)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40685.

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Rural healthcare in India faces critical challenges due to limited infrastructure, a shortage of medical professionals, and difficulties in accessing timely diagnostics. Advancements in Artificial Intelligence (AI) and the Internet of Things (IoT) offer significant potential to bridge gaps in healthcare delivery. This paper examines the integration of AI and IoT technologies in improving healthcare accessibility, focusing on the RogiDoot app—an innovative platform aimed at enhancing healthcare services in rural areas. Drawing on recent advancements in AI-powered diagnostics, IoT-based health m

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Chen, Sheng, Qi Cao, and Yiyu Cai. "Blockchain for Healthcare Games Management." Electronics 12, no. 14 (2023): 3195. http://dx.doi.org/10.3390/electronics12143195.

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More and more serious games have been developed in recent years for patients with aging, rehabilitation, mental, and other healthcare needs. Often, such healthcare games have different experiments or evaluations associated. Rapid advancements in healthcare games see increased regulatory concerns. Unfortunately, there is no authority like the Food and Drug Administration (FDA) in United States for regulatory approvals of healthcare games. Yet, it is not appropriate to use the traditional pharmaceutical FDA approval, which is a tedious and time-consuming process, for healthcare games. We propose

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Sushil Prabhu Prabhakaran. "Integration Patterns in Unified AI and Cloud Platforms: A Systematic Review of Process Automation Technologies." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1932–40. https://doi.org/10.32628/cseit241061229.

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This article comprehensively analyzes unified AI and cloud platforms, examining their role in transforming process automation and decision systems across industries. The article investigates the architectural frameworks and integration patterns that enable the convergence of AI tools, machine learning operations, and workflow orchestration within cloud-native environments. The article explores key innovations, including federated AI implementations, real-time data processing architectures, and multi-cloud integration patterns. It provides insights into their practical applications across finan

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Chrimes, Dillon, Hamid Zamani, Belaid Moa, and Alex Kuo. "Simulations of Hadoop/MapReduce-Based Platform to Support its Usability of Big Data Analytics in Healthcare." Athens Journal of Τechnology & Engineering 5, no. 3 (2018): 197–222. http://dx.doi.org/10.30958/ajte.5-3-1.

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M D, Gangadevi. "BIG DATA ANALYSIS AND CLOUD IN THE APPLICATIONS OF HEALTHCARE." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (2021): 113–18. http://dx.doi.org/10.33564/ijeast.2021.v06i06.015.

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Big Data that either is too large, grows too fast or does not fit into traditional architectures. Within such data, there can be valuable information that discovered through data analysis. Big data now available for analytics present complex, daunting challenges due to the vast number of digital data generated daily by different organizations. The vast amount of data has improved the global community’s ability to defend and allow for progress of rights of vulnerable people around the globe. Moreover, if big data processing is to improve lives, its existing data gathering methods should assist

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Lee, Hee Young, Kang Hyun Lee, Kyu Hee Lee, et al. "Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using MEDBIZ platform." DIGITAL HEALTH 9 (January 2023): 205520762211496. http://dx.doi.org/10.1177/20552076221149659.

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The aim of this study was to introduce the implemented MEDBIZ platform based on the internet of medical things (IoMT) supporting real-time digital health services for precision medicine. In addition, we demonstrated four empirical studies of the digital health ecosystem that could provide real-time healthcare services based on IoMT using real-world data from in-hospital and out-hospital patients. Implemented MEDBIZ platform based on the IoMT devices and big data to provide digital healthcare services to the enterprise and users. The big data platform is consisting of four main components: IoMT

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E. I., Ekoro,, and Agana, M. A. "Healthcare Sensor –Based Physiological Parameters Data Aggregation and Analytics Scheme for Monitoring of Patients in Internet of Medical Things (IoMT) Enabled E-Healthcare Platform." British Journal of Computer, Networking and Information Technology 8, no. 2 (2025): 1–14. https://doi.org/10.52589/bjcnit-vruhjjln.

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Inadequate mechanism for efficient patient health monitoring and data aggregation mechanism in healthcare services has posed a serious bottleneck to healthcare delivery the world over. Healthcare sensors have become an accessible means for the communication of data from patients to medical personnel. The use of healthcare sensors is also an invention in medical practice which involves measuring vital physiological parameters of patients with the objectives of detecting disorders to mitigating them and preventing severe complications. In this research, a system architecture for effective tracki

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