Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients’ responses

Abstract

BACKGROUND: Artificial intelligence (AI) is transforming healthcare, but concerns about algorithmic biases and ethical challenges hinder patient acceptance. This study examined the effects of proactive versus passive algorithmic ethics practices on patient responses across different healthcare engagement types (privacy-focused vs. utility-focused). METHODS: We conducted a 2 × 2 online experiment with 513 participants in China. The experiment manipulated the healthcare provider's algorithmic ethics approach (proactive vs. passive) and the healthcare engagement type (privacy-focused vs. utility-focused). Participants were randomly assigned to view a scenario describing a hospital's AI diagnostic system, then completed measures of attitudes, trust, and intentions to use the AI-enabled service. RESULTS: Proactive algorithmic ethics practices significantly increased positive attitudes, trust, and usage intentions compared to passive practices. The positive impact of proactive practices was stronger for privacy-focused healthcare (e.g., mental health services) compared to utility-focused services emphasizing care optimization. CONCLUSIONS: This study underscores the critical role of proactive, context-specific algorithmic ethics practices in cultivating patient trust and engagement with AI-enabled healthcare. To optimize outcomes, healthcare providers must strategically adapt their ethical governance approaches to align with the unique privacy-utility considerations that are most salient to patients across different healthcare contexts and AI use cases. CLINICAL TRIAL NUMBER: Not applicable.

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