Journal of Patient-Reported Outcomes最新文献

Background: Pelvic floor disorders (PFDs) affect women's physical, psychological, and social well-being, particularly in low- and middle-income countries. The PFIQ-7, a key patient-reported outcome (PROM) measure, assesses symptom severity and quality of life but lacks an Urdu adaptation. This study aims to translate, culturally adapt, and validate the PFIQ-7 for Urdu-speaking women to improve its clinical and research utility.

Methodology: This cross-sectional, observational study was conducted in two phases: Phase 1 focused on translating and culturally adapting the PFIQ-7, while Phase 2 assessed its psychometric properties. A total of 108 Urdu-literate women participated. The PFIQ-7 underwent rigorous translation following international guidelines, including forward and backward translation, consensus meetings, and pilot testing. The study evaluated reliability (Cronbach's alpha, interclass correlation) and validity (content, criterion, and construct validity) through expert review, S-POP-Q correlation, and known-group comparisons.

Results: The Urdu PFIQ-7 demonstrated excellent internal consistency (Cronbach's alpha = 0.884) and test-retest reliability (ICC = 0.995). Strong criterion validity was evidenced by significant correlations between PFIQ-7 scores and S-POP-Q findings (SCC = 0.67; p < 0.005). Construct validity was confirmed, as patients with higher S-POP-Q stages and urinary incontinence reported significantly higher PFIQ-7 scores compared to asymptomatic participants (p < 0.0005). Pilot testing confirmed the questionnaire's clarity, cultural relevance, and feasibility, with a completion time of approximately four to five minutes.

Conclusions: The Urdu PFIQ-7 is a reliable and valid tool for assessing PFD's impact on quality of life, supporting its use in clinical and research settings in Pakistan. Future research should explore its utility in rural and community-based settings to enhance generalizability.

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.