孕产妇死亡是美国的危机。然而more than 60%可以预防孕产妇死亡，并采用正确的循证干预措施。数据是揭示最佳护理实践的强大工具。虽然包括孕产妇健康数据在内的医疗保健数据已大规模生成widespread adoptionand use of Electronic Health Records (EHR), much of this data remains unstandardized and unanalyzed. Further, while many federal datasets related to maternal health are openly available through initiatives set forth in the Open Government National Action Plan, there is no central coordinating body charged with analyzing this breadth of data. Advancing data harmonization, research, and analysis are foundational elements of the Biden Administration’s解决孕产妇健康危机的蓝图。作为数据驱动的技术，人工智能（AI）具有支持孕产妇健康研究工作的巨大潜力。AI有希望应用的示例包括使用电子健康数据predict准妈妈是否在分娩期间有困难的风险。但是，需要进一步的研究来了解如何以促进的方式有效地实施这项技术透明度，安全性和公平性。The Biden-Harris Administration should establish an AI Center of Excellence to bring together data sources and then analyze, diagnose, and address maternal health disparities, all while demonstrating trustworthy andresponsible AI principles。
Maternal deaths currently average around700per year, and severe maternal morbidity-related conditions impact upward of 60,000 women annually. Stark maternal health disparities persist in the United States, and pregnancy outcomes are subject to substantial racial/ethnic disparities, including maternal morbidity and mortality. According to the疾病预防与控制中心(CDC), “Black women are three times more likely to die from a pregnancy-related cause than White women.” Research is ongoing to specifically identify the root causes, which include socioeconomic factors such as insurance status, access to healthcare services, and risks associated with social determinants of health. For example,产妇护理沙漠在孕产妇卫生服务的县中存在，估计有220万童年妇女，存在于全国各地。
Many federal, public, and private datasets exist to understand the conditions that impact pregnant people, the quality of the care they receive, and ultimate care outcomes. For example, the CDC collects abundant data on maternal health,包括怀孕死亡率监视系统（PMSS）和National Vital Statistics System(神)。然而,许多这样的数据集,还没有to be analyzed at scale or linked to other federal or privately held data sources in a comprehensive way. More broadly, an estimated30％的数据generated globally is produced by the healthcare industry. AI is uniquely designed for data management,including cataloging, classification, and data integration。AI将在联邦政府处理前所未有的数据以产生基于证据的建议以改善孕产妇健康成果的能力中发挥关键作用。
AI的应用在整个医疗部门都迅速增殖，因为它们有可能减少医疗支出并改善患者的预后（图1）。该技术的几种应用存在于孕产妇的健康连续性中，如下图所示。例如，有证据表明AI可以帮助临床医生识别的更多70％在妊娠中期，处于妊娠中期的妈妈，通过分析患者数据并识别与健康状况不佳相关的模式。根据其发现，AI可以为患者在发生之前最有可能在怀孕挑战中处于危险中的建议。研究还证明了AI在fetal health monitoring。
然而，出于AI的所有潜力，消费者和医疗提供者对这些算法的工作原理有很大的了解。政策分析师认为，算法中的“算法歧视”和反馈循环（可能会加剧算法偏见）potential risksof using AI in healthcare outside of the confines of an ethical framework. In response, certain federal entities such as the Department of Defense, the Office of the Director of National Intelligence, the National Institute for Standards and Technology, and the U.S. Department of Health and Human Services have published and adopted指南for implementing data privacy practices and building public trust of AI. Further, past Day One authors have proposed the establishment of测试床for government-procured AI models to provide services to U.S. citizens. This is critical for enhancing the safety and reliability of AI systems while reducing the risk of perpetuating existing structural inequities.
It is vital to demonstrate safe, trustworthy uses of AI and measure the efficacy of these best practices through applications of AI to real-world societal challenges. For example, potential use cases of AI for maternal health include asocial determinants of health [SDoH] extractor, which combines AI with clinical notes to more effectively identify SDoH information and analyze its potential role in health inequities. A center dedicated to ethically developing AI for maternal health would allow for the development of evidence-based guidelines for broader AI implementation across healthcare systems throughout the country. Lessons learned from this effort will contribute to the knowledge base around ethical AI and enable development of AI solutions for health disparities more broadly.
Plan of Action
以满足要求推进数据收集、standardization, transparency, research, and analysis to address the maternal health crisis, the Biden-Harris Administration should establish an AI Center of Excellence for maternal health. The AI Center of Excellence for Maternal Health will bring together data sources, then analyze, diagnose, and address maternal health disparities, all while demonstrating trustworthy and responsible AI principles. The Center should be created within the Department of Health and Human Services (HHS) and work closely with relevant offices throughout HHS and beyond, including the HHS Office of the Chief Artificial Intelligence Officer (OCAIO), the National Institutes of Health (NIH) IMPROVE initiative, the CDC, the Veterans Health Administration (VHA), and the National Institute for Standards and Technology (NIST). The Center should offer competitive salaries to recruit the best and brightest talent in AI, human-centered design, biostatistics, and human-computer interaction.
The first priority should be to work with all agencies tasked by the White House Blueprint for Addressing the Maternal Health Crisis to collect and evaluate data. This includes privately held EHR data that is made available through the Qualified Health Information Network (QHIN) and federal data from the CDC, Centers for Medicare and Medicaid (CMS), Office of Personnel Management (OPM), Healthcare Resources and Services Agency (HRSA), NIH, United States Department of Agriculture (USDA), Housing and Urban Development (HUD), the Veterans Health Administration, and Environmental Protection Agency (EPA), all of which contain datasets relevant to maternal health at different stages of the reproductive health journey from Figure 1. The Center should serve as a data clearing and cleaning shop, preparing these datasets using best practices for data management, preparation, and labeling.
The second priority should be to evaluate existing datasets to establish high-priority, high-impact applications of AI-enabled research for improving clinical care guidelines and tools for maternal healthcare providers. These AI demonstrations should be aligned with the White House’s Action Plan and be focused on implementing best practices for AI development, such as the AI Risk Management Framework developed by NIST. The following examples demonstrate how AI might help address maternal health disparities, based on该领域临床医生告知的优先领域：
- AI implementation should be explored for analysis of electronic health records from the VHA and QHIN to predict patients who have a higher risk of pregnancy and/or delivery complications.
- Using VHA data and QHIN data, AI should be explored in supporting patient monitoring in instances of patient referrals and/or transfers to hospitals that are appropriately equipped to serve high-risk patients, following guidelines provided by the American College of Obstetricians and Gynecologists.
- Data on housing from HUD, rural development from the USDA, environmental health from the EPA, and social determinants of health research from the CDC should be connected to risk factors for maternal mortality in the academic literature to create an AI-powered risk algorithm.
The final priority should be direct translation of the findings from AI to federal policymaking around reducing maternal health disparities as well as ethical development of AI tools. Research findings for both aspects of this interdisciplinary initiative should be framed usingLiving Evidence有助于确保研究衍生的证据和指导的模型仍然是最新的。
- Conduct a study on the use cases uncovered for AI to help address maternal health disparities explored through the various demonstration projects.
Successful piloting of the Center could be made possible by passage of an equivalent bill to S.893 in the current Congress. This is a critical first step in supporting this work. In March 2021, the S.893—Tech to Save Moms Act was introduced in the Senate to fund research conducted by National Academies of Sciences, Engineering, and Medicine to understand the role of AI in maternal care delivery and its impact onbias in maternal health。同等法案通过法律将使国家科学，工程和医学学院能够与HHS并行进行研究，以产生更多的发现并扩大潜在的影响。
在所有发达国家中，美国的孕产妇健康差异率最高。然而，可以预防超过60％的与怀孕有关的死亡，这突出了一个关键的机会，可以揭示阻碍整个国家更公平的健康结果的因素。立法支持研究以了解AI在解决孕产妇健康差异方面的作用的研究将确认该国的承诺确保我们准备在21岁时蓬勃发展英石century influenced and shaped by next-generation technologies such as artificial intelligence.
A large portion of gig workers are people of color, and the nature of their temporary and largely unregulated work can leave them vulnerable to economic instability and workplace abuse. To increase protections for fair work, the Department of Labor should create an Office of the Ombudsman for Fair Work.
孕产妇死亡是美国的危机。The Biden-Harris Administration should establish an AI Center of Excellence to bring together data sources and then analyze, diagnose, and address maternal health disparities, all while demonstrating trustworthy and responsible AI principles.
To address problems of algorithmic harm, the National Institute of Standards and Technology (NIST) should invest in the development of comprehensive AI auditing tools, and federal agencies with the charge of protecting civil rights and liberties should collaborate with NIST to develop these tools and push for comprehensive system audits.
在零工经济中工作的人们，技术供应链和其他自动化效果的角色面临着工会工作场所的巨大障碍。这些角色是美国经济中增长最快的部分之一，由BIPOC工人压倒性地填补。拜登 - 哈里斯政府可以通过建立一个国家的诚信基金来促进种族平等，并支持低薪BIPOC工人的工会化工作。