Conditional Optimism and Systemic Constraints: Healthcare Provider Perceptions of AI in Safety-Net Settings Using a Mixed-Methods Vignette Approach
- 1The University of Texas at Austin The IC2 Institute
Abstract
As health AI tools enter clinical spaces, how they are perceived by providers in safety-net environments shapes whether their benefits reach underserved populations. We examined the degree and drivers of healthcare providers trust in health AI, whether that trust moderates perceived benefits for safety-net populations, and how providers balance benefit against concern across four specific hypothetical AI tools. We used a two-study mixed-methods design: 18 semi-structured interviews with healthcare practitioners and research leaders, followed by a survey of 229 Texas healthcare providers incorporating four vignettes describing hypothetical AI tools, each rated for appeal and concern and followed by open-ended explanation. Responses were thematically coded and stratified by self-reported trust in health AI (47.6% high trust, 52.4% low trust). Low-trust respondents most often cited building trust (50.5%) and data bias and accuracy (33.0%) as drivers of their current trust level, while high-trust respondents more often cited efficient and effective care (23.5% vs. 3.7% low-trust, p<0.001) and accuracy of data (11.8% vs. 1.8% low-trust, p=0.004); concerns about data security, governance, and the need for human oversight were raised at similar rates by both groups. When asked how AI could specifically benefit safety-net populations, both groups converged on expanding access to care (45.9% each), and none of 25 benefit-related codes differed significantly by trust group. Across all four vignettes, mean appeal ratings exceeded mean concern ratings, and high-trust providers rated every tool as more appealing and, for three of four tools, less concerning, without changing which tool ranked where relative to others. Providers exhibit conditional optimism: support is contingent on governance, human oversight, and context-sensitive implementation rather than on the technology itself. Realizing AIs promise for safety-net populations will require human-in-the-loop models, localized implementation strategies, community-informed design, and engaging providers directly in policy design and tool development.
Author SummaryArtificial intelligence is increasingly built into the everyday tools that healthcare workers use, but whether it actually helps the patients who need the most support depends on what those workers think of it and how much they trust it. We talked with 18 healthcare workers and researchers, then surveyed 229 healthcare providers across Texas, many of whom work in clinics and hospitals that serve low-income and rural communities. About half of the providers we surveyed trusted health AI and half did not. Providers who trusted AI less pointed to a need to build trust and worries about biased or inaccurate data; providers who trusted it more pointed to gains in efficient, accurate care. Both groups agreed, in similar numbers, that AIs biggest promise for underserved patients is expanding access to care, and neither group described that promise differently from the other. When we showed providers four realistic AI tools, all of them found more to like than to worry about. Our findings suggest that providers are not simply for or against health AI. Their support depends on whether AI is deployed with real human oversight, sound governance, and attention to the specific setting where it is used.
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BibTeX
@misc{purohit2026conditional,
title = {{Conditional Optimism and Systemic Constraints: Healthcare Provider Perceptions of AI in Safety-Net Settings Using a Mixed-Methods Vignette Approach}},
author = {I. Purohit and M. Kammer-Kerwick and E. Spandikow and G. Pogue and S. C. Watkins},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.29.26364281},
url = {https://www.medrxiv.org/content/10.64898/2026.09.29.26364281v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Conditional Optimism and Systemic Constraints: Healthcare Provider Perceptions of AI in Safety-Net Settings Using a Mixed-Methods Vignette Approach
AU - I. Purohit
AU - M. Kammer-Kerwick
AU - E. Spandikow
AU - G. Pogue
AU - S. C. Watkins
PY - 2026
DA - 2026/09/29
PB - medRxiv
DO - 10.64898/2026.09.29.26364281
UR - https://www.medrxiv.org/content/10.64898/2026.09.29.26364281v1
AB - As health AI tools enter clinical spaces, how they are perceived by providers in safety-net environments shapes whether their benefits reach underserved populations. We examined the degree and drivers of healthcare providers trust in health AI, whether that trust moderates perceived benefits for safety-net populations, and how providers balance benefit against concern across four specific hypothetical AI tools. We used a two-study mixed-methods design: 18 semi-structured interviews with healthcare practitioners and research leaders, followed by a survey of 229 Texas healthcare providers incorporating four vignettes describing hypothetical AI tools, each rated for appeal and concern and followed by open-ended explanation. Responses were thematically coded and stratified by self-reported trust in health AI (47.6% high trust, 52.4% low trust). Low-trust respondents most often cited building trust (50.5%) and data bias and accuracy (33.0%) as drivers of their current trust level, while high-trust respondents more often cited efficient and effective care (23.5% vs. 3.7% low-trust, p<0.001) and accuracy of data (11.8% vs. 1.8% low-trust, p=0.004); concerns about data security, governance, and the need for human oversight were raised at similar rates by both groups. When asked how AI could specifically benefit safety-net populations, both groups converged on expanding access to care (45.9% each), and none of 25 benefit-related codes differed significantly by trust group. Across all four vignettes, mean appeal ratings exceeded mean concern ratings, and high-trust providers rated every tool as more appealing and, for three of four tools, less concerning, without changing which tool ranked where relative to others. Providers exhibit conditional optimism: support is contingent on governance, human oversight, and context-sensitive implementation rather than on the technology itself. Realizing AIs promise for safety-net populations will require human-in-the-loop models, localized implementation strategies, community-informed design, and engaging providers directly in policy design and tool development. Author SummaryArtificial intelligence is increasingly built into the everyday tools that healthcare workers use, but whether it actually helps the patients who need the most support depends on what those workers think of it and how much they trust it. We talked with 18 healthcare workers and researchers, then surveyed 229 healthcare providers across Texas, many of whom work in clinics and hospitals that serve low-income and rural communities. About half of the providers we surveyed trusted health AI and half did not. Providers who trusted AI less pointed to a need to build trust and worries about biased or inaccurate data; providers who trusted it more pointed to gains in efficient, accurate care. Both groups agreed, in similar numbers, that AIs biggest promise for underserved patients is expanding access to care, and neither group described that promise differently from the other. When we showed providers four realistic AI tools, all of them found more to like than to worry about. Our findings suggest that providers are not simply for or against health AI. Their support depends on whether AI is deployed with real human oversight, sound governance, and attention to the specific setting where it is used.
ER -