BIG DATA AND PREDICTIVE ANALYTICS IN MEDISENSE FOR CLINICAL DECISION-MAKING
Keywords:
Big Data, Predictive Analytics, Medisense, Clinical Decision-MakingAbstract
Big data and predictive analytics are revolutionizing modern healthcare by enabling accurate, data-driven clinical decision-making. Platforms such as Medisense combine artificial intelligence, electronic health records (EHRs), and predictive modeling to improve diagnostic accuracy, treatment planning, and patient outcomes. This article explores the role of big data and predictive analytics in Medisense, highlighting four core areas: integrating big data for clinical insights, predictive analytics for disease prevention, real-time monitoring and decision support, and addressing challenges of data privacy and governance. Graphical illustrations demonstrate adoption trends, accuracy improvements, efficiency gains, and ethical challenges. The study emphasizes that while Medisense enhances healthcare delivery in Pakistan and beyond, strong ethical and policy frameworks are essential for safe and equitable integration.
References
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare. Health Information Science and Systems.
Khan, H. (2022). Big Data Applications in Pakistan’s Healthcare. Journal of Health Informatics.
Obermeyer, Z., & Emanuel, E. (2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. NEJM.
World Health Organization. (2021). Global Strategy on Digital Health. WHO.
Ali, U. (2021). AI-Driven Clinical Decision-Making in South Asia. Asian Informatics Journal.
Chen, D. (2020). Predictive Analytics for Disease Prevention. Computational Health Review.
Malik, S. (2021). Data Integration in Medisense. Pakistan Journal of Biomedical Engineering.
Javed, M. (2022). Ethics and AI in Healthcare. International Journal of Ethics in Medicine.
Ahmed, F. (2020). Machine Learning in Healthcare Diagnostics. Clinical AI Journal.
UNESCO. (2020). Ethical Dimensions of AI in Healthcare. UNESCO Report.
Alam, S. (2021). Healthcare Data Governance in Pakistan. Policy Review Journal.
Patel, S. (2019). Predictive Models in Chronic Disease Management. Health Informatics Research.
Zhang, L. (2021). Big Data for Population Health. Journal of Global Health.
Fatima, N. (2022). Challenges of Digital Healthcare Adoption in Pakistan. Pakistan Social Sciences Review.
Brown, P. (2020). Data Privacy in Telehealth. Health Policy Journal.
Singh, R. (2021). AI and Early Disease Detection. Clinical Medicine Insights.
Ali, H. (2022). Real-Time Monitoring in Digital Platforms. Biomedical Systems Journal.
World Bank. (2020). Digital Health Infrastructure in Developing Countries. Washington, DC.
Saeed, T. (2021). Algorithmic Bias in Healthcare AI. Ethics and Technology Review.
WHO & ITU. (2022). Artificial Intelligence in Health — Opportunities and Challenges. Joint Report.
Ahmad, N. R. (2024). Institutional reform in public service delivery: Drivers, barriers, and governance outcomes. Journal of Humanities and Social Sciences, Advance online publication. https://doi.org/10.52152/jhs8rn12
Ahmad, N. R. (2025). Urban water service delivery in emerging economies: Fiscal sustainability, cost recovery, and governance performance. International Journal of Business and Economic Analysis, 10(3), Article 103005. https://doi.org/10.24088/IJBEA-2025-103005
Irk, E. (2025). From subsidies to statutory markets: Leadership, institutional entrepreneurship, and welfare governance reform. Journal of Public Policy, 25(3), 123-145. https://doi.org/10.52152/s59sjh53
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