Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: a qualitative study of healthcare professionals' perspectives


Elgin C., Elgin C.

BMC MEDICAL ETHICS, cilt.25, sa.1, 2024 (SCI-Expanded, SSCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1186/s12910-024-01151-8
  • Dergi Adı: BMC MEDICAL ETHICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, IBZ Online, CINAHL, EMBASE, MEDLINE, Psycinfo, Directory of Open Access Journals
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

BackgroundArtificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are increasingly being integrated into healthcare for various purposes, including resource allocation. While these systems promise improved efficiency and decision-making, they also raise significant ethical concerns. This study aims to explore healthcare professionals' perspectives on the ethical implications of using AI-CDSS for healthcare resource allocation.MethodsWe conducted semi-structured qualitative interviews with 23 healthcare professionals, including physicians, nurses, administrators, and medical ethicists in Turkey. Interviews focused on participants' views regarding the use of AI-CDSS in resource allocation, potential ethical challenges, and recommendations for responsible implementation. Data were analyzed using thematic analysis.ResultsParticipant responses are clustered around five pre-determined thematic areas: (1) balancing efficiency and equity in resource allocation, (2) the importance of transparency and explicability in AI-CDSS, (3) shifting roles and responsibilities in clinical decision-making, (4) ethical considerations in data usage and algorithm development, and (5) balancing cost-effectiveness and patient-centered care. Participants acknowledged the potential of AI-CDSS to optimize resource allocation but expressed concerns about exacerbating healthcare disparities, the need for interpretable AI models, changing professional roles, data privacy, and maintaining individualized care.ConclusionsThe integration of AI-CDSS into healthcare resource allocation presents both opportunities and significant ethical challenges. Our findings underscore the need for robust ethical frameworks, enhanced AI literacy among healthcare professionals, interdisciplinary collaboration, and rigorous monitoring and evaluation processes. Addressing these challenges proactively is crucial for harnessing the potential of AI-CDSS while preserving the fundamental values of equity, transparency, and patient-centered care in healthcare delivery.