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Survival rates for people with cancer and quality of life for survivors have increased significantly as a result of innovations in cancer treatment, improvements in early detection, and improved healthcare access. In the United States, 1 in 2 men and 1 in 3 women will be diagnosed with cancer in their lifetime. As more cancer survivors and patients remain in the workforce, employers must evaluate how they can adjust workplace policies to meet employee and business needs. Unfortunately, many people still encounter barriers to remaining in the workplace following a cancer diagnosis for themselves or a loved one. In an effort to explore the impacts of contemporary employment policies on patients with cancer, cancer survivors, and caregivers, NCCN hosted the Policy Summit “Cancer Care in the Workplace: Building a 21st Century Workplace for Cancer Patients, Survivors, and Caretakers” on June 17, 2022. This hybrid event, through keynotes and multistakeholder panel discussions, explored issues regarding employer benefit design, policy solutions, current best and promising practices for return to work, and how these issues impact treatment, survivorship, and caregiving in the cancer community.

Background: Internet-based health education is increasingly vital in patient care. However, the readability of online information often exceeds the average reading level of the US population, limiting accessibility and comprehension. This study investigates the use of chatbot artificial intelligence to improve the readability of cancer-related patient-facing content. Methods: We used ChatGPT 4.0 to rewrite content about breast, colon, lung, prostate, and pancreas cancer across 34 websites associated with NCCN Member Institutions. Readability was analyzed using Fry Readability Score, Flesch-Kincaid Grade Level, Gunning Fog Index, and Simple Measure of Gobbledygook. The primary outcome was the mean readability score for the original and artificial intelligence (AI)–generated content. As secondary outcomes, we assessed the accuracy, similarity, and quality using F1 scores, cosine similarity scores, and section 2 of the DISCERN instrument, respectively. Results: The mean readability level across the 34 websites was equivalent to a university freshman level (grade 13±1.5). However, after ChatGPT’s intervention, the AI-generated outputs had a mean readability score equivalent to a high school freshman education level (grade 9±0.8). The overall F1 score for the rewritten content was 0.87, the precision score was 0.934, and the recall score was 0.814. Compared with their original counterparts, the AI-rewritten content had a cosine similarity score of 0.915 (95% CI, 0.908–0.922). The improved readability was attributed to simpler words and shorter sentences. The mean DISCERN score of the random sample of AI-generated content was equivalent to “good” (28.5±5), with no significant differences compared with their original counterparts. Conclusions: Our study demonstrates the potential of AI chatbots to improve the readability of patient-facing content while maintaining content quality. The decrease in requisite literacy after AI revision emphasizes the potential of this technology to reduce health care disparities caused by a mismatch between educational resources available to a patient and their health literacy.