The Evolving Definition of Health Literacy
In August 2020, the Centers for Disease Control and Prevention (CDC) updated its definition of health literacy, emphasizing the ability to use health information effectively (CDC). This shift highlights that merely understanding medical terminology is insufficient—patients must be able to apply information to make informed health decisions. While this aligns with broader efforts in patient-centered care, substantial barriers remain. Many struggle to interpret dense, highly technical documents such as informed consent forms, patient education materials, and medication instructions. These challenges contribute to medication nonadherence (World Health Organization), poor chronic disease management (National Library of Medicine), and reduced participation in clinical research (National Library of Medicine).
Despite advancements in health literacy principles, readability issues persist. Many informed consent forms (ICFs) remain overly complex and exceed the average patient’s reading level—at a 10th grade level, as opposed to the recommended approximate 6th grade level (National Library of Medicine)—often including excessive procedural details that hinder comprehension (New England Journal of Medicine).
AI’s Role in Bridging the Health Literacy Gap
AI, particularly natural language processing (NLP) and machine learning (ML) models, presents an opportunity to address these challenges by transforming complex medical information into plain language that is more accessible to patients while preserving critical details. This technology has already been implemented in some electronic health record (EHR) systems to provide clearer overviews of diagnoses, lab results, and treatment plans (Topflight). AI-powered chatbots and virtual health assistants offer on-demand explanations of medical terms, treatment regimens, and procedural instructions, improving accessibility, especially for patients with limited health literacy or non-native language speakers (Research Gate; National Library of Medicine).
One real-world application of AI is in improving informed consent forms, which are often dense with legal and regulatory language that overwhelms patients. AI-driven ICF generation has the power to streamline content by extracting key risks, benefits, and procedures into concise, patient-friendly summaries. Additionally, incorporating interactive elements such as voice-read summaries or chatbot support can provide real-time clarification (IBM). Early studies suggest that AI-generated, human-reviewed consent forms lead to higher comprehension rates and improved retention of key study details (PubMed; NPJ Digital Medicine), potentially increasing patient trust and participation in clinical trials.
Human-Centered Design: The Key to Effective AI Integration
The success of AI in improving health literacy depends on human-centered design (HCD), which ensures that content is structured in a way that aligns with key usability principles. A foundational principle of HCD is information chunking, which breaks down complex concepts into manageable sections. This improves retention and comprehension by helping users process information more easily. Readability optimization is also critical—AI-generated summaries should adhere to established readability guidelines, prioritizing plain language over technical jargon while maintaining medical accuracy.
Multimodal communication further enhances engagement by integrating text, visuals, and audio, catering to different learning preferences. For example, AI-driven patient education tools may include infographics, interactive diagrams, and voice-read summaries, making medical information more accessible to a broader range of users. Finally, iterative user feedback ensures that AI-generated content continues to evolve based on real-world usage. Continuous validation and refinement with input from medical professionals and patients ensure that information remains accurate, culturally sensitive, and contextually appropriate.
HCD is deeply aligned with lean principles, which emphasize efficiency, waste reduction, and streamlined processes (Scrum Alliance). By embedding HCD into AI-driven tools, organizations can not only improve patient outcomes but also enhance internal efficiency in content development, reducing unnecessary revisions and ensuring clarity from the outset. This approach benefits both patients and healthcare organizations by improving workflow, reducing confusion, and ensuring compliance with readability standards.
Challenges and Ethical Considerations
Despite its promise, AI-enabled health communication comes with challenges. Algorithmic bias remains a concern; if AI models are not trained on diverse datasets, they risk perpetuating disparities in health literacy by favoring certain demographics. Additionally, AI-driven personalization relies on patient data, making data privacy and security critical considerations. Compliance with regulations such as FDA and HIPAA must be ensured, integrating robust oversight and validation processes into AI-driven health communication tools. Over-reliance on AI should also be avoided—while AI can enhance readability and comprehension, human expertise remains essential in reviewing and refining content to ensure it meets regulatory, ethical, and contextual standards.
A Call to Action for Industry Leaders
For decision-makers in the pharmaceutical and biotech industries, addressing health literacy challenges through AI requires a balanced, evidence-based approach. Organizations developing patient-facing content should incorporate AI-driven solutions that integrate human-centered design principles, ensuring accessibility across diverse populations. Regulatory teams should collaborate with AI developers to refine consent forms and educational materials, leveraging AI’s ability to simplify language while maintaining compliance. Additionally, industry stakeholders should support research initiatives that assess the effectiveness of AI in health communication. By taking a proactive approach, industry leaders can contribute to improving patient outcomes while advancing ethical and transparent healthcare communication.
Conclusion
The CDC’s redefined concept of health literacy highlights an urgent need for more effective, patient-friendly health communication strategies. AI, when combined with human-centered design principles, has the potential to bridge this gap by transforming complex medical information into clear, actionable insights. By leveraging AI’s capabilities while maintaining human oversight, we can enhance patient understanding, improve adherence to treatment plans, and ultimately drive better health outcomes. As we look ahead, the challenge is not just in adopting AI for health literacy but in doing so responsibly—ensuring that the technology is accessible, unbiased, and aligned with real patient needs. With careful implementation, AI can serve as a powerful ally in making healthcare more equitable, transparent, and patient-focused.
Author Bio
Jeanette Towles, CEO and owner of Synterex, Inc
Jeanette Towles is the Chief Executive Officer and owner of Synterex, Inc., a woman-owned, disability-owned clinical and regulatory consulting firm specializing in agile-based project management methodology, automation, and AI-driven technologies. Prior to that, she held in-house consulting and FTE medical writing and clinical science positions at both small- and large-sized companies, including managing a group of programmers and vendors working on automated documents, with cumulative industry experience of nearly 20 years. She lives in the Boston area with her husband, 2 children, and dog.
COI Disclosure: Jeanette Towles is President and CEO of Synterex, Inc., a clinical and regulatory consulting firm specialising in AI-driven technologies and health literacy documentation. This disclosure is included in the interest of editorial transparency.
Disclaimer: This guest commentary reflects the author’s analysis and is provided for informational purposes only; it does not constitute medical, legal, or official editorial advice from Life Science Daily News.
