The Lucerne Toolbox 3 initiative addresses the pressing need for evidence-based integration of digital health and artificial intelligence (AI) technologies in early breast cancer care. The multidiscip...

Social justice, Digital Equity, Poverty, Accessible Technology, Marginalized communities, Pacific, COFA, Veterans, Low-Income, Affordable housing

ActiGraph, which makes digital tools used in clinical trials, acquired the life sciences business of Biofourmis, once considered a digital health unicorn.

Here we propose six recommendations for researchers to consider for the successful implementation of evidence-based digital health interventions into routine clinical practice.

mHealth Applications Market Share, Trends and Growth Analysis By Type (Monitoring Applications, Diagnosis & Treatment, Education & Awareness, Healthcare...

As concerns grow around counterfeit and illicit active pharmaceutical ingredients in the US supply chain, Hims & Hers has established safeguards to support transparency. In her latest article, ou...

Global Interactive Patient Engagement and Clinical Workflow Solution Transforms Patient and Clinician Engagement

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.