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Who should decide how limited healthcare resources are prioritized? Autonomous technology as a compelling alternative to humans

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  • Jonathan J Rolison
  • Peter L T Gooding
  • Riccardo Russo
  • Kathryn E Buchanan

Abstract

Who should decide how limited resources are prioritized? We ask this question in a healthcare context where patients must be prioritized according to their need and where advances in autonomous artificial intelligence-based technology offer a compelling alternative to decisions by humans. Qualitative (Study 1a; N = 50) and quantitative (Study 1b; N = 800) analysis identified agency, emotional experience, bias-free, and error-free as four main qualities describing people’s perceptions of autonomous computer programs (ACPs) and human staff members (HSMs). Yet, the qualities were not perceived to be possessed equally by HSMs and ACPs. HSMs were endorsed with human qualities of agency and emotional experience, whereas ACPs were perceived as more capable than HSMs of bias- and error-free decision-making. Consequently, better than average (Study 2; N = 371), or relatively better (Studies 3, N = 181; & 4, N = 378), ACP performance, especially on qualities characteristic of ACPs, was sufficient to reverse preferences to favor ACPs over HSMs as the decision makers for how limited healthcare resources should be prioritized. Our findings serve a practical purpose regarding potential barriers to public acceptance of technology, and have theoretical value for our understanding of perceptions of autonomous technologies.

Suggested Citation

  • Jonathan J Rolison & Peter L T Gooding & Riccardo Russo & Kathryn E Buchanan, 2024. "Who should decide how limited healthcare resources are prioritized? Autonomous technology as a compelling alternative to humans," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-34, February.
    • Handle: RePEc:plo:pone00:0292944
    DOI: 10.1371/journal.pone.0292944
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    References listed on IDEAS

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    3. Berger, Benedikt & Adam, Martin & Rühr, Alexander & Benlian, Alexander, 2021. "Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124219, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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