Comparing patient education tools for chronic pain medications: Artificial intelligence chatbot versus traditional patient information leaflets

Author:

Gondode Prakash1ORCID,Duggal Sakshi1ORCID,Garg Neha1ORCID,Sethupathy Surrender1ORCID,Asai Omshubham2ORCID,Lohakare Pooja3ORCID

Affiliation:

1. Department of Anesthesiology Pain Medicine and Critical Care, All India Institute of Medical Sciences (AIIMS) New Delhi, India

2. Department of Anesthesiology, All India Institute of Medical Sciences, Nagpur, Maharashtra, India

3. Department of Microbiology, Mahatma Gandhi Institute of Medical Sciences, Wardha, Maharashtra, India

Abstract

Background and Aims: Artificial intelligence (AI) chatbots like Conversational Generative Pre-trained Transformer (ChatGPT) have recently created much buzz, especially regarding patient education. Such informed patients understand and adhere to the management and get involved in shared decision making. The accuracy and understandability of the generated educational material are prime concerns. Thus, we compared ChatGPT with traditional patient information leaflets (PILs) about chronic pain medications. Methods: Patients' frequently asked questions were generated from PILs available on the official websites of the British Pain Society (BPS) and the Faculty of Pain Medicine. Eight blinded annexures were prepared for evaluation, consisting of traditional PILs from the BPS and AI-generated patient information materials structured similar to PILs by ChatGPT. The authors performed a comparative analysis to assess materials’ readability, emotional tone, accuracy, actionability, and understandability. Readability was measured using Flesch Reading Ease (FRE), Gunning Fog Index (GFI), and Flesch-Kincaid Grade Level (FKGL). Sentiment analysis determined emotional tone. An expert panel evaluated accuracy and completeness. Actionability and understandability were assessed with the Patient Education Materials Assessment Tool. Results: Traditional PILs generally exhibited higher readability (P values < 0.05), with [mean (standard deviation)] FRE [62.25 (1.6) versus 48 (3.7)], GFI [11.85 (0.9) versus 13.65 (0.7)], and FKGL [8.33 (0.5) versus 10.23 (0.5)] but varied emotional tones, often negative, compared to more positive sentiments in ChatGPT-generated texts. Accuracy and completeness did not significantly differ between the two. Actionability and understandability scores were comparable. Conclusion: While AI chatbots offer efficient information delivery, ensuring accuracy and readability, patient-centeredness remains crucial. It is imperative to balance innovation with evidence-based practice.

Publisher

Medknow

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5. BPS Patient Publication

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