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              <text>Pandey, Ananya; Joseph, Jipson</text>
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              <text>Navigating the healthtech ecosystem: Challenges and roadblocks</text>
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              <text>Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare;pp.31-48</text>
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              <text>&lt;a href="https://doi.org/10.4018/979-8-3373-0081-8.ch002" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/979-8-3373-0081-8.ch002&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105010402119?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105010402119?origin=resultslist&lt;/a&gt;</text>
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              <text>Pandey A., Christ University, Bangalore, India; Joseph J., Christ University, Bangalore, India</text>
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              <text>Driven by recent technological advancements like Artificial Intelligence, blockchain, and digital twins, the healthcare sector is revolutionizing at a fast pace. HealthTech has numerous applications such as robotic surgery, virtual assistants, telemedicine and fitness tracking devices. But this data-driven innovation in healthcare sector is severely hampered by government barriers, privacy and security issues. The potential for comprehensive patient care is limited by interoperability problems across different healthcare systems, which impede smooth data interchange. Additionally, access to HealthTech solutions is limited by the digital divide. Many healthcare providers face issues with high developmental challenges, implementation costs, and required infrastructure investments. Considering the above perspective, this chapter examines the challenges and roadblocks in the HealthTech ecosystem. To address these issues and promote a strong HealthTech ecosystem, the authors critically analyze the role of healthcare providers, tech firms, and patients in view of an all-inclusive approach.  2025 by IGI Global Scientific Publishing. All rights reserved.</text>
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              <text>ISBN: 979-833730083-2; 979-833730081-8;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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