How AI is transforming individualized member education in population health management

Author’s note: I would like to thank my AI assistant for helping me craft this post on the role and value of AI.

Artificial intelligence (AI) has the remarkable ability to process extensive data, extract insights and tailor educational content to meet the specific needs of patients. It is reshaping clinical practices and revolutionizing the way health care providers deliver patient education. This personalized approach is particularly pivotal in the population health management space as it relates to chronic disease management, where ongoing member engagement and adherence to care plans are critical.

The expanding role of generative AI is transformative but must also be subject to limitations.

In this context, the expanding role of generative AI is transformative but must also be subject to limitations. Generative AI can analyze patient data, including medical history and personal health preferences, to generate tailored educational materials. This not only enhances the learning experience for the members but also ensures that the information is relevant and easily comprehensible.

However, the limitations of generative AI cannot be overlooked. Despite its advanced algorithms, generative AI may lack the nuanced understanding that clinicians possess, especially in interpreting complex medical conditions and emotional nuances of members. Additionally, the quality of output from generative AI heavily depends on the quality and quantity of the input data, which raises concerns about data biases and inaccuracies. While generative AI offers promising prospects in revolutionizing member and patient education, its deployment must be carefully managed, considering these limitations and ethical considerations.¹

At the forefront of this innovation, ActiveHealth is developing patient education content recommender systems, using AI technology to prioritize clinician-curated educational content based on individual health risk factors and medical diagnoses. These systems aim to:

• Automate and optimize content delivery
• Complement human-led member coaching with smart, scalable digital tools and strategies
• Provide a more personalized and effective member experience
• Enhance the value delivered to both members and clients

Furthermore, AI’s potential to integrate social determinants of health could enhance population health outcomes. It provides a clinical-community linkage that is crucial for effective health education and management.² By considering factors such as socioeconomic status, lifestyle and environment, AI can offer more holistic and individualized educational interventions for patients.

AI’s integration into individualized patient education signifies a new era in health care. This evolution promises enhanced patient engagement, improved health literacy and optimized health outcomes. The future of health care is likely to witness AI becoming increasingly ingrained in patient education strategies. The role of AI in revolutionizing patient education within population health management is set to expand, paving the way for a more informed and health-conscious population. This will not only benefit people in managing their health more effectively but also empower members of the health care team to deliver more targeted and effective educational interventions.

 

¹ Paranjape, K. Implementing Artificial Intelligence in Health Care: Data and Algorithm Challenges and Policy Considerations. American Journal of Biomedical Science & Research. January 8, 2021. Accessed January 17, 2024.

² Kwon, IG; Kim, SH; and Martin D. Integrating Social Determinants of Health to Precision Medicine through Digital Transformation: An Exploratory Roadmap. International Journal of Environmental Research and Public Health. May 10, 2021. Accessed January 17, 2024.