Research Fellow: AI-moderated Interviews at Scale: The Trust in Healthcare Study

Job description

Surgo Health is requesting proposals for research projects building on our dataset focused on the healthcare trust landscape. While declining public trust in the U.S. healthcare system is widely documented through conventional survey methods, the underlying, individual-level drivers of this erosion often remain unclear. To address this gap, our dataset explores trust dynamics across medical providers, health institutions, social media, and artificial intelligence, while highlighting actionable pathways for restoring confidence. The data encompasses both a detailed questionnaire battery and in-depth qualitative dialogues gathered by Derin®, our AI-moderated interviewing system. Designed as a randomized, counterbalanced study, the dataset enables direct methodological comparisons between standard survey approaches and AI-moderated conversational interviews. It spans roughly 2,000 respondents across two distinct research panels. It has 6,000 completed interviews alongside extensive survey batteries, standard demographic indicators, and population weighting factors. We are seeking research proposals that: - **Advance the narrative on trust & healthcare** —contribute to contemporary thinking on this subject in the U.S. - **Leverage the conversations** — projects should examine the reasoning, experiences, or context revealed when respondents elaborate, going beyond what closed items already capture. - **Use the dataset** — research questions should be answerable with the current dataset and not require funding for new data collection - **Publishable** — proposals should aim for peer-reviewed publication, with rigorous methodology. We encourage proposals that move beyond showing that conversational interviews produce more detail, focusing instead on for whom, under what conditions, and through what mechanisms conversational data adds value. Projects will be given access to the full dataset and documentation, and can integrate external data (such as ACS, provider density, or state health policy indicators) via the harmonized geographic and demographic variables in the dataset.