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To Build Trust in Health Innovation, Listen to Patients and Learn from Their Real-World Evidence Journeys.

Background Terheyden et al. recently described a compelling vision for large language model-enabled patient-reported outcome measures (LLM-PROMs). Main text We support Terheyden et al.’s vision and offer complementary observations about the potential for generative artificial intelligence (GenAI) in assessing patient-centered outcomes. GenAI has the potential to improve the quality and efficiency of developing traditional PROMs and collecting patient experience data. Traditional PROMs rely on standardized questions and responses, which may introduce ambiguity about the health concept being assessed. Yet, interviewers who are trained in the meaning of the concepts can tailor questions to the respondent’s experience and conversation style and have a back-and-forth clarification of meaning to ensure that both the interviewer’s and respondent’s meanings are aligned. The shortcoming of this approach is that it cannot be done at scale with human interviewers. However, trained GenAI interviewers could make such an assessment a reality for large samples of patients. The technology is already available to train GenAI interviewers in interview technique, the intent of each item, and a consistent approach toward coding the respondent’s answer based on the conversation. Conclusion The health outcomes research field should actively inquire into what patient experience data can be collected via GenAI and rigorously evaluate the quality of the assessments obtained.

I write about nature, but when Milly got sick with a mystery illness, it never occurred to me that a long-forgotten tick bite could be the cause

PDF | On Aug 8, 2023, Marion Brown and others published Book 'Withdrawal from prescribed psychotropic drugs' Edited by Peter Lehmann & Craig Newnes Chapter 23: The patient voice: Antidepressant withdr...