Patient-Centered Research Through Artificial Intelligence to Identify Priorities in Cancer Care.

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Abstract

Patient-centered ÌÇÐÄ´«Ã½ is essential for bridging the gap between ÌÇÐÄ´«Ã½ and patient care, yet patient perspectives are often inadequately represented in health ÌÇÐÄ´«Ã½.To leverage artificial intelligence (AI) and natural language processing (NLP) to analyze a large dataset of patient messages, defining patient concerns and generating relevant ÌÇÐÄ´«Ã½ topics, and to quantify the quality of these AI-generated topics.This case series was conducted using an automated framework involving a 2-staged unsupervised NLP topic model and AI-generated ÌÇÐÄ´«Ã½ topic suggestions. The study was based on deidentified patient portal message data from individuals with breast or skin cancer at ÌÇÐÄ´«Ã½ and 22 affiliated centers over July 2013 to April 2024.A widely used large language model (ChatGPT-4o [OpenAI]; April 2024) was used and guided through multiple prompt-engineering strategies to perform multilevel tasks, including knowledge interpretation and summarization (eg, interpreting and summarizing the NLP-defined topics), knowledge generation (eg, generating ÌÇÐÄ´«Ã½ ideas corresponding to patients' issues), self-reflection and correction (eg, ensuring and revising the ÌÇÐÄ´«Ã½ ideas after searching for scientific articles), and self-reassurance (eg, confirming and finalizing the ÌÇÐÄ´«Ã½ ideas).Three breast oncologists (J.L.C., A.W.K., F.R) and 3 dermatologists (K.Y.S, J.Y.T., E.L.) evaluated the meaningfulness and novelty of the AI-generated ÌÇÐÄ´«Ã½ topics using a 5-point Likert scale (1 representing exceptional to 5 representing poor). Mean (SD) scores for meaningfulness and novelty were computed for each topic.A total of 614?464 patient messages were analyzed from 25?549 individuals, 10?665 with breast cancer (98.6% female) and 14?884 had skin cancer (49.0% female). The overall mean (SD) scores for meaningfulness and novelty were 3.00 (0.50) and 3.29 (0.74), respectively, for breast cancer topics and 2.67 (0.45) and 3.09 (0.68), respectively, for skin cancer topics. One-third of the AI-suggested ÌÇÐÄ´«Ã½ topics were highly meaningful and novel when both scores were lower than the average (5 of 15 for breast cancer and 6 of 15 for skin cancer). Notably, two-thirds of the AI-suggested topics were novel (10 of 15 for breast cancer and 11 of 15 for skin cancer).This case series demonstrates that AI/NLP-driven analysis of large volumes of patient messages can generate quality ÌÇÐÄ´«Ã½ topics in cancer care that reflect patient perspectives, providing valuable guidance for future patient-centered health ÌÇÐÄ´«Ã½ endeavors.

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