Artificial intelligence (AI) has been opening new doors in workflows across industries in the last few years, and healthcare is among the sectors exploring what areas AI can support. For organizations managing healthcare language access plans, that may include finding more efficient ways to handle growing volumes of multilingual content while still protecting the clarity and accuracy patients rely on.
The potential benefits are significant, but healthcare content also presents considerations that make AI adoption particularly sensitive. Understanding those risks is an important first step in determining where AI belongs in a workflow and what safeguards need to surround its use.
A Thoughtful Approach to Incorporating AI in Workflows
Introducing AI to healthcare communication requires a thoughtful approach that looks beyond what technology can accelerate and considers where it may introduce new vulnerabilities. As Colleen Beres, chief revenue officer at Terra, puts it, “As AI adoption gains momentum, so does risk exposure.” Two particularly important areas to consider are:
- Protected health information (PHI): Sensitive patient information may enter the content workflow at different stages. If PHI is incorporated into an AI tool without appropriate controls, organizations may lose the ability to oversee how that information is handled or whether the system is appropriate for it. Missteps can create privacy and security concerns and affect patient trust.
- Reliability across languages: AI systems may have less-representative or less-reliable data for languages of limited diffusion (LLD), and underrepresented or unreliable data can affect the quality of their output. In patient-facing content, technically fluent language is not enough if meaning, terminology, or appropriateness remain unreliable. Scenarios like these make qualified human oversight particularly important.
PHI and reliability risks do not rule out AI’s place in healthcare communication, but they do make clear why its use needs defined boundaries and safeguards from the start.
Governance Makes AI Easier to Use Responsibly
A safer approach to AI in healthcare communication begins by setting clear expectations about how AI tools can be used, and who is accountable for those decisions. As Colleen puts it, “You govern it and you get help.” For organizations managing a healthcare language access plan, that means identifying where PHI enters the content workflow and defining appropriate boundaries before AI becomes part of the process.
In preparation for such a merger, teams should be able to answer a few practical workflow questions:
- Tool approval: Has this system been vetted for the type of content involved?
- Data control: What happens to the information once the tool receives it?
- User access: Who is allowed to use the tool for this workflow?
- Human review: When should qualified experts evaluate the output before content moves forward?
Checks like these help prevent organizations from treating all AI tools as interchangeable. A system that is appropriate for general drafting, for example, may not be suitable for content involving PHI.
Governance also makes it easier to determine when outside expertise is useful. A vetted language partner like Terra can help organizations assess how AI best fits within an existing content workflow while keeping linguistic requirements and information-handling practices in view. In this model, Terra’s role is not to introduce AI to every process, but to act as a strategic partner that helps make informed decisions about whether its use is appropriate, and if so, what safeguards should surround it.
Conclusion
The question about the relationship between AI and healthcare is not simply whether AI can support healthcare communication, but under what conditions it should be used. For organizations managing multilingual content, those conditions should be defined before sensitive information ever enters an AI-enabled workflow.
When governance is built into the language access plan from a project’s beginning, AI can support communication without teams losing sight of the responsibility their organizations have to the patients who depend on it.
