The Patient Protection and Affordable Care Act (“Affordable Care Act” or ACA), health information technology (HIT) adoption, and increasing implementation of electronic medical records, are all propelling health care into the world of big data. Big data, analytics, and predictive algoithms are poised to play a large part in the transformation of health-care delivery in the United States, determining who will benefit and, unfortunately, who may suffer from its insights. Health-care reform depends on cost savings derived from the application of sophisticated data analytics to the ever-expanding mass of data collected from and about individual patients. Health data analytics can lead to improved care, new scientific discoveries, and better medical treatment. Encouraging healthy behaviors, eliminating health disparities, and addressing the underlying determinants of health in society are important national goals. It is unclear, however, whether massive data collection about personal health and individual social status, both within the health-care system and outside of it, will serve the goal of addressing historical discrimination in health care, or whether data analytics will lead to the loss of individual privacy, unequal treatment of individuals, and the perpetuation of health inequality. Data amassed from electronic health records (EHRs), private sector health website visits, personal health devices, mobile health applications, and social networks, are being linked together in a big data environment. Secondary use of health data by employers, insurers, marketers, and others heightens concerns. The collection and use of massive amounts of data about individuals, fed into a fragmented health analytics framework, may impose personal and societal costs if not carefully constructed. Furthermore, a predictive analytics environment in health care may affect some groups differently than others, not decreasing health dis-parities but segmenting populations and resulting in differential care. Health-care providers and policy makers should ask hard questions about how harms to personal privacy can be avoided, stigmas prevented, and threats of unbridled commercialization ameliorated.

中文翻译：

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健康的预测？卫生保健中数据分析的问题

《患者保护和负担得起的医疗法案》（“ Affordable Care Act”或ACA），健康信息技术（HIT）的采用以及电子病历的不断实施，都在推动医疗保健进入大数据世界。大数据，分析和预测算法已准备好在美国医疗服务的转型中扮演重要角色，确定谁将受益，不幸的是，谁可能会从其见解中受苦。医疗保健改革取决于将复杂的数据分析应用于从个体患者以及与个体患者有关的不断增长的海量数据中所节省的成本。健康数据分析可以带来更好的护理，新的科学发现和更好的治疗。鼓励健康的行为，消除健康差异，解决社会中健康的根本决定因素是重要的国家目标。但是，目前尚不清楚，无论是在医疗保健系统内还是在医疗保健系统外，有关个人健康和个人社会地位的大量数据收集是否将有助于解决医疗保健方面的历史歧视，或者数据分析是否会导致医疗保健系统的发展。个人隐私的丧失，个人的不平等待遇以及健康不平等的长期存在。在大数据环境中，将从电子健康记录（EHR），私营部门健康网站访问，个人健康设备，移动健康应用程序和社交网络收集的数据链接在一起。雇主，保险公司，市场商人等对健康数据的二次使用引起了人们的关注。如果不精心构建，则收集和使用大量有关个人的数据，将其输入零散的健康分析框架中可能会造成个人和社会成本。此外，医疗保健中的预测分析环境可能会影响某些群体，而不会降低其他群体的健康差距，而是将人群进行细分，从而导致差别保健。卫生保健提供者和政策制定者应就如何避免对个人隐私的伤害，避免污名化以及缓解无限制商业化的威胁提出疑问。并没有减少健康差距，而是将人群进行了细分，从而产生了差别待遇。卫生保健提供者和政策制定者应就如何避免对个人隐私的伤害，避免污名化以及缓解无限制商业化的威胁提出疑问。并没有减少健康差距，而是将人群分割并导致了差别护理。卫生保健提供者和政策制定者应就如何避免对个人隐私的伤害，避免污名化以及缓解无限制商业化的威胁提出疑问。