A Review of Data Intelligence Applications Within Healthcare Sector in the United States

Clement Odooh, Regina Robert, Efijemue Oghenekome Paul
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Abstract

Data intelligence technologies have transformed the United States healthcare sector, bringing about transformational advances in patient care, research, and healthcare management. United States is the focus due fact that many academic and research institutions in the country are at the forefront of healthcare data research, making it an attractive location for in-depth studies.This paper explores the diverse realm of Data Intelligence in Healthcare, examining its applications, challenges, ethical considerations, and emerging trends. Data Intelligence Applications encompass a spectrum of technologies designed to collect, process, analyze, and interpret data effectively. These apps enable healthcare practitioners to make more educated decisions, forecast health outcomes, manage population health, customize treatment, optimize workflows, assist research, improve data security, and drive healthcare analytics. However, the use of data intelligence applications raises issues and concerns about data privacy, fairness, transparency, data quality, accountability, fair data access, regulatory compliance, and the balance between automation and human judgment. Emerging themes include AI and machine learning domination, stronger ethical and regulatory frameworks, edge and quantum computing, data democratization, sustainability applications, and developing human-machine collaboration. Data intelligence has an impact that goes beyond healthcare delivery, influencing decision-making, scientific discovery, education, and economic growth. Understanding its potential and ethical responsibilities is paramount as data-driven insights redefine healthcare excellence and extend their influence across sectors.
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美国医疗保健行业数据智能应用综述
数据智能技术改变了美国的医疗保健行业,为病人护理、研究和医疗保健管理带来了变革性的进步。美国之所以成为研究重点,是因为美国的许多学术和研究机构都处于医疗保健数据研究的前沿,这使美国成为进行深入研究的一个具有吸引力的地点。本文探讨了医疗保健中数据智能的各种领域,研究了其应用、挑战、伦理考虑和新兴趋势。数据智能应用包括一系列旨在有效收集、处理、分析和解释数据的技术。这些应用程序使医疗从业人员能够做出更明智的决策、预测健康结果、管理人群健康、定制治疗方案、优化工作流程、协助研究、提高数据安全性并推动医疗分析。然而,数据智能应用的使用引发了有关数据隐私、公平性、透明度、数据质量、问责制、公平数据访问、监管合规性以及自动化与人工判断之间平衡的问题和担忧。新出现的主题包括人工智能和机器学习主导、更强大的道德和监管框架、边缘和量子计算、数据民主化、可持续性应用以及发展人机协作。数据智能的影响超出了医疗保健服务的范围,它影响着决策、科学发现、教育和经济增长。随着数据驱动的洞察力重新定义医疗保健的卓越性并将其影响力扩展到各个领域,了解其潜力和道德责任至关重要。
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