New paper in Value in Health (w/ Wirth, Edwards & Reeve): FDA's PFDD Guidance 3 adopts the rationale-based approach to validation for COAs. We offer two strategies and generic starting points for constructing a sound rationale for PROs, ObsROs, ClinROs, and PerfOs. doi.org/10.1016/j.jv...

One of my favorite books. He published a collection of source material that was very good as well.

Artificial intelligence and the future of patient-centered outcomes - Journal of Patient-Reported Outcomes 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.

New commentary with Bryce Reeve in JPRO: “Artificial intelligence and the future of patient-centered outcomes.”

GenAI could transform how we capture the patient voice but we must proceed with care.

Link: doi.org/10.1186/s416...

#PROMs #PatientVoice #GenerativeAI #FutureOfHealthcare #DigitalHealth

Thanks for sharing these beautiful pictures, Frank.

They’re all good, but this was especially good.

This one was a little too close to home....

RFK Jr. & HHS: Last Week Tonight with John Oliver (HBO) YouTube video by LastWeekTonight

Outstanding

youtu.be/8H34jcpEsFs?...

3/n

Grateful for our stellar Advisory Board:
• John Andrejack
• Elizabeth (Nicki) Bush
• Bill Byrom
• Robyn Carson
• Cheryl Coon
• Steve Grambow
• Chris Lindsell
• Lola Rahib
• Bryce Reeve

More to come as we develop and share new training resources.

2/n

Leading this initiative with an incredible team from the FDA, Vector Psychometric Group, Symphony Learning, UNC-Chapel Hill, Triangle CERSI, and Duke’s Center for Health Measurement.

Thrilled to kick off the PARCR Project—a 3-year FDA-funded collaboration to advance patient-centered clinical research by creating training on methods related to the FDA’s Patient-Focused Drug Development Guidance Series (bit.ly/42N5mFm).

1/n

#PFDD #ClinicalResearch #PatientCentered #COA #eCOA

Humbling indeed! I had to look it up as well.

Thanks for your interest, Beatriz!

5/5

(4) In a parallel groups design, meaningful within-patient change is not especially relevant for understanding the meaningfulness of a treatment effect. @stephensenn.bsky.social @f2harrell.bsky.social

4/5

(3) Who provides input and what types of anchor variables are used to generate points of reference might differ for interpreting individual- versus population-level estimates of treatment effect.

3/5

(2) Points of reference (a.k.a. “thresholds”) may be different for interpreting individual- and population-level treatment effect estimates.

2/5

(1) Instead of talking about a “between-group difference,” specify the level at which you wish to infer a treatment effect: population or individual. Treatment effects for both levels can be estimated from a parallel groups trial design.

New publication commenting on approaches to understand the meaningfulness of treatment effects on endpoints based on clinical outcome assessments (COAs). Sorting out "between-group difference," "meaningful within-group change," and more.

rdcu.be/eg5x7

1/5 Summary to follow...