A technical guide for healthcare organizations balancing the use of AI in documentation. This article was not generated by AI, but AI was used in helping to outline it and expound upon terminology.
Artificial intelligence like ChatGPT is affecting just about every aspect of business operations today. AI is a wonderful tool, but it’s critical to use it like a tool instead of depending on it completely to create content and write policies. This is especially true for the healthcare industry, which deals with sensitive data, HIPAA requirements, and the need for precise documentation.
Security policies in particular need to be carefully drafted with a human eye, but AI can make this process more efficient when used carefully. This article will explain how you can use AI to support the drafting of security policies in a healthcare setting, and what aspects of the drafting and review process should be left to human supervision instead.
Understanding the Role of Security Policies in Healthcare
Security policies are sets of documented rules, guidelines, and procedures that inform members of an organization how to protect data and resources. Policies outline expectations for user behavior, data handling, incident response and containment, and overall security measures. Essentially, they are plans to help prevent security incidents from happening.
In the healthcare industry, it’s crucial for organizations to keep sensitive patient information private and protected. To stay compliant with HIPAA, you should have clear security policies based on your organization’s members and their various roles.
Below are some common types of security policies:
- Access control. This defines who can access specific resources within an organization and what they can do with those resources. This includes establishing who can access sensitive information, systems, and physical locations.
- Data encryption. This type of policy dictates when and what type of data should be encrypted to protect its confidentiality. A data encryption policy ensures HIPAA compliance and determines the differing levels of data sensitivity and the types of encryption for each.
- Incident response. In the unfortunate event that a security incident does occur, an incident response policy can help contain the issue and mitigate damage to reputation and assets. Communication plans can be outlined in the policy to specify how information will be shared internally and externally during an incident, including who needs to be informed and when.
- Device management. Every organization and the individuals who are part of it use devices like smartphones, laptops, and tablets. This policy provides a framework for using the devices securely, keeping them updated, outlining password requirements, and accessing business networks responsibly.
- Training and awareness. It’s essential to educate employees about security-related topics, like security risks, compliance, how to respond to phishing attempts, and reputation management. This policy describes how such training will be given, how often, and the consequences of non-compliance with the policy.
Where AI Can Help
AI tends to work well for basic or foundational writing, and that’s why it’s a great tool for drafting policy templates. Letting AI outline your policies saves you the time of having to “reinvent the wheel” of a tried-and-true policy structure. The specific needs of your organization can be added in as needed, and nuanced details can be modified later.
AI tools like Grammarly can be used to standardize the language of your policies. If your style guide uses specific terminology or requires a particular tone throughout your content, AI tools can point out issues with it and help you preserve consistency.
Regulations and compliance are extremely important in the healthcare industry, and AI can help automate compliance mapping and align your content with regulatory frameworks. You can use AI models to help you check all your policies to satisfy the requirements of HIPAA, NIST, HITECH, or whatever applies to your organization.
Ensuring compliance and covering the legal aspects of your policies is critical, but it’s also important to make the policies clear and understandable to all levels of understanding in your organization. Under your supervision, AI tools can summarize technical text and condense it into actionable policy sections. This way, the technical aspects of your policies are in place, and your organization’s members are clear on how to follow them.
Risks and Limitations of AI in Policy Writing
Like all tools, AI cannot perfectly replace human judgment and oversight. There are some lacking attributes of AI that need to be understood while outlining and refining policies:
- Contextual misunderstanding. AI can get you started outlining the general aspects of policies, but it lacks full knowledge of your unique organization’s infrastructure and workflows. It’s essential for policies to be reviewed by humans who fully understand the context of the policies and the organization’s specific needs surrounding them.
- Inaccurate or non-compliant output. Despite AI’s knowledge of laws like HIPAA and HITECH, it may not fully satisfy the laws when mapping them out in the policies. Legal language is something that should be double- and triple-checked to make sure that no compliance requirement goes overlooked.
- Data privacy concerns. Any data you feed into AI models has the risk of being retained, memorized, trained with, and leaked through a future prompt. Use AI to tailor the language of your policies, but never give it sensitive details, protected information, or proprietary data.
- Lack of authority and accountability. Security policies aren’t just informational—they are enforceable rules. These policies should reflect the organizational leadership’s own risk tolerance and ethical standards, and AI cannot and should not make decisions that could leave your organization liable. The policy writer—not the AI model—is accountable for any contradiction or vague language left in a policy.
A human should carefully oversee the compliance standards, clarity, and legal security of policies at every step of their drafting process. Ultimately, there is no accountability trail for an AI model. The fine details of a policy belong fully to the security teams of the organization who approved it.
When Not to Use AI
AI models make the process of drafting policies much more efficient, but they should be set aside completely when the policies are ready for approval, publication, and release to the organization as official materials. Always require a strict and thorough review from security and legal teams before signing off on them.
You should also refrain from using AI models for unique or sensitive policies. AI simply does not have the nuanced contextual understanding required for sensitive edge cases or organization-specific scenarios. In healthcare security documentation, this is a critical limitation, especially with healthcare’s unique needs related to ethical considerations, legal obligations, and risk assessments. An AI model may generate a policy that sounds reasonable but misses crucial details like state laws and regulations or the unique standards of the establishment. If an aspect of a policy covers a high-risk scenario, it’s best to be fully written by a human.
Another situation AI should have no part of is real-time crisis or incident response documents. The sensitivity and risk of these situations is something that requires a human touch, both internally and externally. AI models do not have the judgment and contextual awareness needed for dealing with incidents, and your organization could suffer reputation damage if your public statements addressing a crisis sound insensitive or robotic.
Best Practices for Using AI Safely
Due to their nature as large language models, AI models focus on patterns over factual accuracy, and they can sometimes hallucinate completely false information. When it comes to creating security policies for your healthcare organization, following these best practices will help you get the most out of AI without compromising on compliance or safety:
- Use AI for first drafts, not final versions.
- Combine with human subject matter experts, especially on sensitive policies.
- Use on-premises or private AI models when possible to limit spread of information.
- Maintain version control and audit trails for each policy.
- Conduct periodic manual reviews and updates as needed.
Conclusion
AI can be a powerful tool for accelerating the creation of security policies, especially when used to draft templates and ensure consistency. But in the healthcare industry, where privacy, compliance, and patient trust are essential, AI models should be used with caution. AI cannot replace human judgment or contextual understanding, and it cannot be held accountable for mistakes. If you use AI as a tool and not a substitute, you can write efficient, trustworthy policies for your organization that can keep your members informed and your information protected.







