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Big Data in Healthcare: Leveraging Analytics for Better Patient Outcomes
Author(s) | Goutham Bilakanti |
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Country | India |
Abstract | Big data analysis is transforming healthcare by enhancing predictive analytics, optimization of hospital functions, and clinical decision-making operations. The enormous amounts of healthcare information, collected from electronic health records (EHRs), wearable devices, and genomic studies, provide valuable insights that improve patient outcomes and maximize resource deployment. Predictive analytics facilitates the timely identification of diseases, personalized treatment regimens, and preventive patient care, reducing hospital readmissions and healthcare costs. But integration of big data within healthcare systems comes with challenges, such as interoperability, data privacy, and ethical issues with patient confidentiality and informed consent. Data quality and reliability maintenance are also challenging when drawing the appropriate clinical conclusions. Despite these limitations, machine learning and artificial intelligence (AI) advancements have also improved the application of big data in healthcare, for instance, real-time decision support and precision medicine. The future of big data analytics will be vital in shaping the healthcare of the future, foster innovation, enhance patient care, and tackle public health issues more effectively. |
Keywords | Big Data, Healthcare Analytics, Predictive Analytics, Clinical Decision Support, Machine Learning, Electronic Health Records (EHR), Privacy Concerns, Interoperability, Precision Medicine, Artificial Intelligence, Data Integration, Patient Outcomes, Healthcare Optimization, Ethical Considerations |
Field | Computer > Data / Information |
Published In | Volume 1, Issue 1, September 2020 |
Published On | 2020-09-03 |
Cite This | Big Data in Healthcare: Leveraging Analytics for Better Patient Outcomes - Goutham Bilakanti - IJLRP Volume 1, Issue 1, September 2020. DOI 10.5281/zenodo.15196671 |
DOI | https://doi.org/10.5281/zenodo.15196671 |
Short DOI | https://doi.org/g9d633 |
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IJLRP DOI prefix is
10.70528/IJLRP
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