⚡ Quick Summary
A recent systematic review explored how artificial intelligence (AI) can enhance the readability of patient educational materials (PEMs). The findings suggest that AI can significantly improve health literacy, with some models achieving 100% readability scores post-simplification.
🔍 Key Details
- 🗓️ Publication Period: January 2019 to June 2023
- 📚 Focus: AI’s role in simplifying PEMs
- 🔍 Methodology: Systematic review and narrative synthesis
- 📊 Number of Studies Included: 20
- 🧩 Key Themes Identified: Reproducibility, accessibility, emotional support, readability, data security, accuracy, and comprehensiveness
🔑 Key Takeaways
- 📈 AI Simplification: AI effectively simplified PEMs, achieving reproducibility rates up to 90.7%.
- 👍 User Satisfaction: Over 85% of users reported satisfaction with AI-generated materials.
- 📖 Readability Improvements: AI models, particularly ChatGPT, achieved 100% readability scores after simplification.
- ⚖️ Mixed Performance: AI’s accuracy and reliability varied, with some limitations in comprehensiveness.
- 💬 Soft Skills Gap: AI models struggled with personalization and soft skills.
- 🔧 Future Enhancements: Higher-caliber models and prompt engineering could address current limitations.
- 🌍 Health Literacy Potential: AI has the potential to significantly enhance patient health literacy through improved PEMs.
📚 Background
Understanding health information is vital for patients to make informed decisions about their care. However, many existing patient educational materials are often too complex, leading to misunderstandings and poor health outcomes. The integration of artificial intelligence in simplifying these materials presents a promising solution to enhance health literacy and improve patient engagement.
🗒️ Study
This systematic review aimed to evaluate the effectiveness of AI in simplifying PEMs. The researchers analyzed studies published between January 2019 and June 2023, focusing on various AI modalities and their impact on readability, accuracy, and user satisfaction. An inductive thematic approach was employed to identify common themes across the selected studies.
📈 Results
The review revealed that AI models could significantly enhance the readability of PEMs, with some achieving 100% readability after simplification. User satisfaction rates were notably high, exceeding 85%. However, the performance of AI in terms of accuracy and reliability was inconsistent, particularly when dealing with complex medical topics. While AI effectively simplified basic tasks, it often lacked the necessary soft skills and personalization to fully meet patient needs.
🌍 Impact and Implications
The findings of this review highlight the transformative potential of AI in healthcare, particularly in improving patient education. By simplifying complex medical information, AI can empower patients to better understand their health, leading to improved health outcomes. As AI technology continues to evolve, there is a significant opportunity to refine these models, enhancing their accuracy and reliability for more effective patient communication.
🔮 Conclusion
This systematic review underscores the promising role of artificial intelligence in enhancing the readability of patient educational materials. While there are challenges to overcome, particularly regarding accuracy and personalization, the potential benefits for patient health literacy are substantial. Continued research and development in this area could lead to more effective communication strategies in healthcare, ultimately improving patient outcomes.
💬 Your comments
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Can artificial intelligence improve patient educational material readability? A systematic review and narrative synthesis.
Abstract
Enhancing patient comprehension of their health is crucial in improving health outcomes. The integration of artificial intelligence (AI) in distilling medical information into a conversational, legible format can potentially enhance health literacy. This review aims to examine the accuracy, reliability, comprehensiveness and readability of medical patient education materials (PEMs) simplified by AI models. A systematic review was conducted searching for articles assessing outcomes of use of AI in simplifying PEMs. Inclusion criteria are as follows: publication between January 2019 and June 2023, various modalities of AI, English language, AI use in PEMs and including physicians and/or patients. An inductive thematic approach was utilised to code for unifying topics which were qualitatively analysed. Twenty studies were included, and seven themes were identified (reproducibility, accessibility and ease of use, emotional support and user satisfaction, readability, data security, accuracy and reliability and comprehensiveness). AI effectively simplified PEMs, with reproducibility rates up to 90.7% in specific domains. User satisfaction exceeded 85% in AI-generated materials. AI models showed promising readability improvements, with ChatGPT achieving 100% post-simplification readability scores. AI’s performance in accuracy and reliability was mixed, with occasional lack of comprehensiveness and inaccuracies, particularly when addressing complex medical topics. AI models accurately simplified basic tasks but lacked soft skills and personalisation. These limitations can be addressed with higher-calibre models combined with prompt engineering. In conclusion, the literature reveals a scope for AI to enhance patient health literacy through medical PEMs. Further refinement is needed to improve AI’s accuracy and reliability, especially when simplifying complex medical information.
Author: [‘Nasra M’, ‘Jaffri R’, ‘Pavlin-Premrl D’, ‘Kok HK’, ‘Khabaza A’, ‘Barras C’, ‘Slater LA’, ‘Yazdabadi A’, ‘Moore J’, ‘Russell J’, ‘Smith P’, ‘Chandra RV’, ‘Brooks M’, ‘Jhamb A’, ‘Chong W’, ‘Maingard J’, ‘Asadi H’]
Journal: Intern Med J
Citation: Nasra M, et al. Can artificial intelligence improve patient educational material readability? A systematic review and narrative synthesis. Can artificial intelligence improve patient educational material readability? A systematic review and narrative synthesis. 2024; (unknown volume):(unknown pages). doi: 10.1111/imj.16607