Enhancing Patient Education through Automated Generation of Lay Definitions for Medical Jargon

Kernekoncepter

Automated generation of context-aware, patient-friendly lay definitions for complex medical terms can significantly improve patient comprehension and engagement with their health information.

Resumé

This paper introduces a new task of automatically generating lay definitions to simplify complex medical terms into patient-friendly language. The authors created the README dataset, a large collection of over 50,000 unique (medical term, lay definition) pairs and 300,000 mentions, with context-aware lay definitions manually annotated by domain experts.

To improve the quality of the dataset, the authors developed a data-centric Human-AI pipeline called Examiner-Augmenter-Examiner (EAE), which leverages human experts and AI models to filter, augment, and select high-quality data. The authors then used README as training data for models and employed a Retrieval-Augmented Generation (RAG) method to reduce hallucinations and improve the quality of model outputs.

The extensive automatic and human evaluations demonstrate that open-source, mobile-friendly models can achieve or even exceed the performance of state-of-the-art closed-source large language models like ChatGPT when fine-tuned with high-quality data. This research represents a significant step in bridging the knowledge gap in patient education and advancing patient-centric healthcare solutions.

Statistik

The average Flesch-Kincaid Grade Level of online health education materials is post-secondary or higher education, while the average readability of a US adult is 7-8th grade level.
The README dataset contains over 51,623 unique (medical jargon term, lay definition) pairs and 308,242 data points, each consisting of a clinical note context, a medical jargon term, and its corresponding lay definition.

Citater

"The advancement in healthcare transcends medical breakthroughs, encompassing an increased emphasis on patient involvement in self-care."
"One significant barrier persists in the form of medical jargon in EHRs, impeding patient understanding and self-care."
"To bridge this gap, we have engaged medical experts to meticulously curate lay definitions for jargon terms found in NoteAid-MedJEx, targeting a comprehension level suitable for individuals with a 7th to 8th-grade education."

Vigtigste indsigter udtrukket fra

README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP

by Zonghai Yao,... kl. arxiv.org 04-17-2024

https://arxiv.org/pdf/2312.15561.pdf

Dybere Forespørgsler

How can the interactive capabilities of large language models like ChatGPT be leveraged to further enhance patient education and engagement?

Large language models like ChatGPT can be leveraged to enhance patient education and engagement by incorporating interactive features that allow patients to ask questions, seek clarifications, and receive tailored responses. This interactive approach can help patients actively engage with the information provided, leading to better comprehension and retention of medical knowledge. ChatGPT can be used to create interactive patient education tools that simulate conversations, provide personalized explanations, and offer real-time feedback to address patient queries and concerns. By integrating ChatGPT into patient-centric healthcare solutions, healthcare providers can offer a more engaging and interactive educational experience, ultimately improving patient outcomes and satisfaction.

What potential biases may be present in the data used to train the lay definition generation models, and how can these biases be mitigated?

Biases in the data used to train lay definition generation models may stem from various sources, such as the expertise of the annotators, the selection of medical terms, and the context in which the lay definitions are generated. Biases can manifest in the form of cultural, gender, or racial biases, as well as linguistic biases that may impact the quality and accuracy of the lay definitions. To mitigate these biases, it is essential to ensure diverse representation among the annotators, carefully select the medical terms included in the dataset, and validate the lay definitions for accuracy and inclusivity. Additionally, implementing bias detection algorithms and conducting regular bias audits can help identify and address any biases present in the data and models used for lay definition generation.

How can the lay definition generation system be integrated with other patient-centric healthcare technologies, such as mobile health applications, to provide a more comprehensive and personalized patient education experience?

The lay definition generation system can be integrated with other patient-centric healthcare technologies, such as mobile health applications, to provide a more comprehensive and personalized patient education experience. By incorporating the lay definitions into mobile health apps, patients can access simplified explanations of medical terms directly on their smartphones or tablets, enhancing their understanding of health information. Additionally, the system can be designed to provide real-time definitions based on the specific context of the patient's health records or queries, offering personalized educational content tailored to individual needs. Integration with mobile health applications can also enable features like voice assistance, interactive quizzes, and progress tracking to further engage patients and empower them to take control of their health. Overall, integrating the lay definition generation system with mobile health technologies can enhance patient education, promote health literacy, and improve patient outcomes.