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 healthca...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | BMC Medical Ethics |
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| Online Access: | https://doi.org/10.1186/s12910-025-01236-y |
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| author | Sheng Shu Qinglin Luo Zhiqing Chen |
| author_facet | Sheng Shu Qinglin Luo Zhiqing Chen |
| author_sort | Sheng Shu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d8df41ae482a44dbbf29549dbeb8eb41 |
| institution | DOAJ |
| issn | 1472-6939 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Ethics |
| spelling | doaj-art-d8df41ae482a44dbbf29549dbeb8eb412025-08-20T03:10:32ZengBMCBMC Medical Ethics1472-69392025-06-0126111310.1186/s12910-025-01236-yProactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients’ responsesSheng Shu0Qinglin Luo1Zhiqing Chen2School of Management, Chongqing University of TechnologySchool of Economics and Management, Changsha University of Science and TechnologySchool of Management, Chongqing University of TechnologyAbstract 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.https://doi.org/10.1186/s12910-025-01236-yAlgorithmic ethicsPrivacy-utility tradeoffPatient responseHealthcare engagement typeAI in healthcare |
| spellingShingle | Sheng Shu Qinglin Luo Zhiqing Chen Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients’ responses BMC Medical Ethics Algorithmic ethics Privacy-utility tradeoff Patient response Healthcare engagement type AI in healthcare |
| title | Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients’ responses |
| title_full | Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients’ responses |
| title_fullStr | Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients’ responses |
| title_full_unstemmed | Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients’ responses |
| title_short | Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients’ responses |
| title_sort | proactive vs passive algorithmic ethics practices in healthcare the moderating role of healthcare engagement type in patients responses |
| topic | Algorithmic ethics Privacy-utility tradeoff Patient response Healthcare engagement type AI in healthcare |
| url | https://doi.org/10.1186/s12910-025-01236-y |
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