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Paper Title: BIG DATA-DRIVEN INSIGHTS FOR EQUITABLE HEALTHCARE ACCESS AND QUALITY FOR U.S IMMIGRANTS.
Authors Name: Joseph Kobi , Amida Nchaw Nchaw , Brian Otieno
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IJRTI_190164
Published Paper Id: IJRTI2407046
Published In: Volume 9 Issue 7, July-2024
DOI:
Abstract: Immigrant communities in the United States face significant barriers in accessing affordable, high-quality healthcare. Language gaps, lack of insurance coverage, confusion navigating the complex healthcare system, and cultural differences often prevent immigrants from receiving the care they need. These inequities have real human costs in terms of health outcomes and community well-being. However, new approaches leveraging big data analytics show promise in generating insights that can help remove barriers and promote healthcare equity for immigrant populations. Healthcare providers and researchers are now able to capture vast amounts of patient data through electronic health records, insurance claims, mobile health applications, and broader socioeconomic indicators. When analyzed using advanced data mining and machine learning techniques, these diverse datasets have the potential to reveal useful patterns and predict future needs. For example, analyzing usage patterns could identify underserved immigrant groups in specific regions that are delayed or missing important preventive care. Other projects aim to match low-income immigrants with low-cost provider options or translate online health information into multiple languages. Naturally, there are also important privacy and ethical considerations regarding the collection and deployment of sensitive personal health and demographic details like immigration status. An equitable big data approach requires that all information be properly de-identified and used to expand access rather than facilitate exclusion or discrimination. Additional policy recommendations include expanding public health insurance eligibility, increasing community health worker roles, and addressing immigration data gaps. Early case studies also demonstrate how healthcare organizations leveraging data analytics have succeeded in improving key access and quality metrics for immigrant populations. With careful planning and oversight, sharing insights and predictive models between regions could help spread such best practices more broadly. However, overcoming ongoing challenges will require diverse teams able to avoid unintended harms and truly understand immigrant community needs. When implemented with a focus on social good rather than profit, a data-driven approach holds promise to provide novel insights unlocking equitable universal healthcare for all people regardless of immigration status. Now is the time for researchers, innovators and policymakers to work together to make this vision a reality.
Keywords: Big Data, Healthcare Access, Healthcare Quality, Immigrants, United States, Data Analytics, Machine Learning, Equitable Care.
Cite Article: "BIG DATA-DRIVEN INSIGHTS FOR EQUITABLE HEALTHCARE ACCESS AND QUALITY FOR U.S IMMIGRANTS.", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 7, page no.392 - 408, July-2024, Available :http://www.ijrti.org/papers/IJRTI2407046.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2407046
Registration ID:190164
Published In: Volume 9 Issue 7, July-2024
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Page No: 392 - 408
Country: Worcester, Massachusetts, United States
Research Area: Science & Technology
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2407046
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2407046
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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