The following is a guest article by Tim Boltz, Healthcare Solutions Executive at Carahsoft

Artificial intelligence is the most disruptive technology since the widespread adoption of the internet. AI could permeate industries the same way, transforming their operations. Healthcare is not immune.

自互联网被广泛采用以来，人工智能是最具破坏性的技术。人工智能可以以同样的方式渗透到各个行业，改变它们的运营。医疗保健并非免疫。

Health institutions of all types are already exploring AI’s potential to refine patient treatments, relieve the burdens of workforce shortages, and accelerate new research discoveries. Some in the healthcare landscape are already running pilots to determine use case possibilities.

所有类型的卫生机构都在探索人工智能的潜力，以改进患者治疗，减轻劳动力短缺的负担，并加速新的研究发现。医疗保健领域的一些人已经在进行试点，以确定用例的可能性。

Deploying AI solutions in this rightfully risk-averse industry will require addressing longstanding security issues and equity concerns before its full promise can be fulfilled.

在这个合理规避风险的行业部署人工智能解决方案，需要解决长期存在的安全问题和股权问题，才能实现其全部承诺。

Fortifying Health Networks

加强卫生网络

AI projects among healthcare institutions are attracting interest and funding, but the industry needs to overhaul its cybersecurity posture to prevent threats to patient safety and privacy. Healthcare institutions work with a treasure trove of data, harnessing all four data types—Personally Identifiable Information (PII), Payment Card Industry (PCI) data, Protected Health Information (PHI), and Intellectual Property (IP)—making these organizations prime targets for cybercriminals. .

医疗机构中的人工智能项目正在吸引人们的兴趣和资金，但该行业需要彻底改变其网络安全态势，以防止对患者安全和隐私的威胁。医疗保健机构利用大量数据，利用所有四种数据类型-个人识别信息（PII）、支付卡行业（PCI）数据、受保护的健康信息（PHI）和知识产权（IP）-使这些组织成为网络犯罪的主要目标。。

Most healthcare facilities run networks composed of outdated legacy systems and a huge number of internet-connected devices, like imaging machines and monitors, that create an Internet of Medical Things (IoMT). The result is a highly vulnerable attack surface rich with valuable patient, financial, and research information.

大多数医疗机构运行的网络由过时的遗留系统和大量互联网连接设备（如成像机和显示器）组成，这些设备创建了医疗物联网（IoMT）。结果是一个高度脆弱的攻击面，其中包含有价值的患者，财务和研究信息。

Highly organized hackers deploy ransomware, knowing the sensitivity and urgency of operations make healthcare organizations a high-value target. .

高度有组织的黑客部署勒索软件，知道操作的敏感性和紧迫性，使医疗保健组织成为高价值目标。。

Healthcare organizations, however, often do not have the budget to replace outmoded equipment or invest in the latest cyber advancements. Many are still recovering from the lasting effects of the pandemic and a workforce shortage that extends to their IT departments.

然而，医疗保健组织通常没有预算来更换过时的设备或投资于最新的网络进步。许多人仍在从大流行的持久影响和延伸到其IT部门的劳动力短缺中恢复过来。

To dictate the standards for responsible protection of networks and deployment of new technologies, the Federal government must step in. In addition to helping providers find the funds to secure this critical sector, agencies should also lead efforts to create more cybersecurity standards and playbooks for responding to incidents.

为了规定负责任地保护网络和部署新技术的标准，联邦政府必须介入。除了帮助提供商找到资金来保护这一关键部门外，各机构还应带头努力制定更多的网络安全标准和应对事件的行动手册。

The Department of Health and Human Services’ release of a risk management framework is a good start. Still, the healthcare industry would benefit from the sector-specific security standards that the government’s Cybersecurity Maturity Model Certification program is creating for the defense industrial base. .

卫生与公众服务部发布的风险管理框架是一个良好的开端。。。

Robust security practices would deter would-be attackers, and healthcare organizations could focus on providing patients with high-quality care, furthering research efforts, and embracing new technology.

强大的安全措施将阻止潜在的攻击者，医疗保健组织可以专注于为患者提供高质量的护理，进一步的研究工作，并接受新技术。

Ensuring Equitable Access and Outcomes

确保公平的机会和结果

AI in healthcare is still nascent, but equity must be a top consideration in building policies and projects. Emerging technologies like AI offer a chance to design systems for healthcare delivery and clinical research with equity principles built in.

人工智能在医疗保健领域仍处于起步阶段，但在制定政策和项目时，公平必须是首要考虑因素。人工智能等新兴技术为设计内置公平原则的医疗保健和临床研究系统提供了机会。

Some worry that AI could exacerbate health disparities. For example, organizations could develop technology that is informed by large data sets that could breed inequality at the institutional level, especially when those datasets are unvetted.

一些人担心人工智能会加剧健康差距。例如，组织可以开发由大型数据集提供信息的技术，这些数据集可能会在机构层面产生不平等，特别是当这些数据集不受限制时。

However, many see the technology as a way to mitigate inequities. For patients, AI tools could more accurately spot and predict illness from images or create personalized treatment options. Even more simply, calling offices with AI tools that optimize schedules could decrease wait times and improve appointment availability.

然而，许多人认为这项技术是缓解不公平现象的一种方式。对于患者来说，AI工具可以更准确地从图像中发现和预测疾病，或者创建个性化的治疗选择。更简单地说，使用优化时间表的AI工具给办公室打电话可以减少等待时间并提高预约可用性。

After all, getting access to a doctor is the first step of any treatment plan..

毕竟，接触医生是任何治疗计划的第一步。。

For medical providers, AI can help unburden overworked staff. Automation of administrative tasks can shift physicians and other frontline staff from paperwork to focusing on delivering excellent patient care. AI can also help overcome staffing challenges, bolstering recruitment and retention efforts with tools that screen job descriptions to remove biased language or enhance training programs.

对于医疗提供者来说，人工智能可以帮助减轻过度工作的员工的负担。行政任务的自动化可以将医生和其他一线员工从文书工作转变为专注于提供优质的患者护理。人工智能还可以帮助克服人员配置方面的挑战，通过筛选职位描述以消除偏见语言或加强培训计划的工具来加强招聘和保留工作。

In back offices, AI could help refine grant proposals, detect fraud, and accelerate reimbursements..

在后台，人工智能可以帮助改进拨款方案，发现欺诈行为，并加速报销。。

Unlocking AI’s Potential

释放AI的潜力

To embrace AI’s full potential, we need support from regulatory bodies, innovation from the private sector, and also participation from the public, as developing an AI tool without data is impossible.

为了充分发挥人工智能的潜力，我们需要监管机构的支持，私营部门的创新以及公众的参与，因为开发没有数据的人工智能工具是不可能的。

Data fuels projects that could predict the next big outbreak, giving medical professionals time to prepare and refine response plans. Key regulations like HIPAA have yet to be modernized to address potential AI uses. Regulatory bodies should take time to review that the rules address new technological developments. .

数据推动了可以预测下一次大疫情的项目，为医疗专业人员准备和完善应对计划提供了时间。HIPAA等关键法规尚未现代化，以解决潜在的人工智能用途。监管机构应该花时间审查这些规则是否涉及新的技术发展。。

In December 2023, President Joe Biden issued an executive order for HHS to lead policy and regulatory efforts to pave the way for safe, secure, and trustworthy AI use in healthcare settings. However, health-focused agencies have the opportunity to do more to drive adoption, such as incentivizing with Medicare and Medicaid reimbursements. .

2023年12月，美国总统乔·拜登（JoeBiden）发布了一项行政命令，要求卫生与公众服务部（HHS）领导政策和监管工作，为在医疗保健环境中安全可靠地使用人工智能铺平道路。然而，以健康为重点的机构有机会采取更多措施来推动采用，例如通过医疗保险和医疗补助报销来激励。。

What can be done with artificial intelligence is still being discovered. By working together to improve security and ensure equity, the federal government and healthcare organizations can pave the way for revolutionary new treatments and improved health outcomes.

人工智能可以做什么仍在被发现。通过共同努力提高安全性和确保公平，联邦政府和医疗保健组织可以为革命性的新疗法和改善健康结果铺平道路。

TagsAI AI Tools Artificial Intelligence Carahsoft Cybersecurity Datasets health networks Healthcare AI Healthcare Cybersecurity Healthcare Data Healthcare Equity Tim Boltz

TagsAI AI工具人工智能Carahsoft网络安全数据集健康网络医疗保健AI医疗保健网络安全医疗保健数据医疗保健股票Tim Boltz

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