The results of our qualitative study revealed a complex landscape of perspectives among healthcare professionals regarding the ethical implications of AI-driven Clinical Decision Support Systems (AI-CDSS) in healthcare resource allocation. Through our analysis of the interview data, we identified several key themes that elucidate the views, concerns, and recommendations of our participants.
Our study included 23 healthcare professionals from diverse backgrounds and specialties. The sample comprised 10 physicians, 6 nurses, 4 healthcare administrators, and 3 medical ethicists. The average age of participants was 42 years (range: 28–61), with a mean of 15 years of professional experience (range: 3–35). Participants represented various healthcare settings, including academic medical centers, community hospitals, and private practices, ensuring a broad spectrum of perspectives. Table 1 provides information about participants characteristics.
Participants in this study were healthcare professionals with experience or knowledge of AI-CDSS implementation in their practice. This criterion was crucial for ensuring that participants could provide informed perspectives on the ethical implications of AI-CDSS in healthcare resource allocation. Specifically, participants were selected based on their involvement in clinical decision-making processes where AI-CDSS was integrated. Participants were identified through a combination of methods: (1) contacts with major hospitals in the country that had implemented AI-CDSS systems within the past three years, (2) referrals from a member of the Turkish Medical Informatics Association, and (3) snowball sampling where initial participants recommended colleagues with relevant experience. The recruitment process involved sending formal invitations to potential participants, followed by screening interviews to verify their AI-CDSS experience. Some had directly used AI-CDSS tools, while others had participated in decision-making or oversight roles related to the adoption and deployment of these technologies in healthcare settings. Recruitment was conducted using a purposive sampling strategy, targeting individuals from diverse professional backgrounds (e.g., physicians, nurses, administrators, and medical ethicists) to capture a range of insights. Participants were identified through professional networks, medical associations, and institutional affiliations known to be early adopters of AI technologies. Invitations were sent to prospective participants via email, detailing the study objectives and inclusion criteria. To ensure credibility, we selected participants with at least two years of clinical experience and prior exposure to discussions or decisions regarding AI-CDSS. While participants were not required to have experience specifically with AI-CDSS for resource allocation, all were familiar with the broader use of AI-CDSS in clinical settings. Among those interviewed, several described scenarios where AI-CDSS influenced resource distribution indirectly—such as triaging patients or prioritizing diagnostic interventions—providing valuable reflections on resource allocation ethics.
Had we selected participants with less direct knowledge or practical experience with AI-CDSS, the findings might have emphasized general attitudes toward AI rather than nuanced ethical reflections grounded in clinical practice. For instance, individuals with limited familiarity may have raised broader concerns about AI technology or speculative scenarios rather than focusing on the intersection of AI-CDSS and resource allocation. Thus, our sampling approach was designed to elicit rich, experience-based insights while acknowledging that future studies might benefit from contrasting these perspectives with those of less experienced or skeptical stakeholders.
Participants’ responses clustered around five predetermined thematic areas, reflect the key ethical challenges identified in previous literature on AI-CDSS implementation. Their reflections provided valuable insights into how these challenges manifest in daily clinical practice. We should also mention that all quotes presented in this paper have been edited for clarity and readability, ensuring they are concise and accessible to readers while retaining the original meaning. Minor adjustments, such as the removal of filler words or grammatical corrections, were made to improve flow and coherence without altering the content or context of participants’ statements. To maintain transparency, we acknowledge this editing process and provide some unedited, verbatim quotes in the appendix to illustrate participants’ real-time struggles in articulating their reflections and grappling with the ethical dilemmas posed by AI-CDSS.
Theme 1: balancing efficiency and equity in resource allocation
A predominant theme that emerged from our analysis was the tension between the potential for AI-CDSS to improve healthcare efficiency and the concern for maintaining equitable access to care. Many participants acknowledged the potential of AI-CDSS to optimize resource allocation through data-driven decision-making. For instance, a hospital administrator (Participant 7) stated, “AI systems can process vast amounts of data to identify areas where resources are being underutilized or overutilized, potentially leading to more efficient allocation.” This perspective was particularly common among participants in administrative roles, who frequently highlighted the system’s ability to identify inefficiencies in resource distribution.
However, this optimism was consistently tempered by concerns about the potential for AI-CDSS to exacerbate existing healthcare disparities. A medical ethicist (Participant 18) cautioned, “If we’re not careful, these systems could inadvertently prioritize resources towards populations that are already well-served, further marginalizing vulnerable groups.” This concern was shared across different professional roles, with participants expressing particular worry about automated decision-making potentially disadvantaging certain patient populations.
The need for careful design and implementation of AI-CDSS emerged as a crucial subtheme, with participants emphasizing that efficiency gains should not compromise equity. A primary care physician (Participant 3) suggested, “We need to build safeguards into these systems to actively counteract existing biases and prioritize equitable access to care.” Several participants described developing their own informal protocols to review AI recommendations, particularly for cases involving traditionally underserved populations.
The tension between efficiency and equity manifested differently across various healthcare settings. Participants from resource-constrained settings, such as those from small-city hospitals, consistently prioritized equity concerns over efficiency gains. For example, Participant 15, a critical care nurse, remarked: “In settings like ours, efficiency is meaningless unless equity is addressed. AI has the potential to widen the gap, so we consciously adjust how we use it to serve our most vulnerable patients first.” This perspective was echoed by other participants working in similar settings, who described developing specific strategies to ensure AI recommendations didn’t disadvantage their vulnerable patient populations.
Many participants also noted the practical challenges of balancing these competing priorities in daily practice. They described various informal approaches to mediating between AI recommendations and equity considerations, such as additional review processes for certain patient groups, regular team discussions about AI recommendations, and maintaining manual oversight of resource allocation decisions. These practical strategies revealed how healthcare professionals actively work to maintain equity while leveraging the efficiency benefits of AI-CDSS.
Theme 2: transparency and explicability of AI-CDSS
Another significant theme that emerged was the importance of transparency and explicability in AI-CDSS used for resource allocation decisions. Participants consistently expressed the need to understand how these systems arrive at their recommendations, particularly when they influence decisions about patient care and resource distribution. This concern was especially pronounced among clinicians who regularly needed to communicate AI-assisted decisions to patients and their families.
A neurologist (Participant 12) emphasized, “If I’m going to rely on an AI system to help me make decisions about resource allocation, I need to be able to understand and explain its reasoning to my patients and colleagues.” This sentiment was echoed across different specialties, with many participants describing specific instances where they struggled to explain AI-generated recommendations to stakeholders.
Several participants raised concerns about the “black box” nature of some AI algorithms and its implications for ethical decision-making. An oncologist (Participant 9) noted, “There’s a risk of deferring too much to these systems without truly understanding their limitations or potential biases.” This concern was particularly acute in cases involving complex resource allocation decisions, where participants reported feeling uncomfortable making decisions they couldn’t fully explain or justify.
To address these transparency challenges, participants described developing various informal and formal strategies. These included creating simplified explanation frameworks for patients, maintaining detailed records of override decisions, and establishing peer review processes for AI recommendations. A healthcare administrator (Participant 20) proposed, “We need to develop a culture of ‘AI literacy’ among healthcare providers, where understanding and critically evaluating these systems becomes a core competency.” Several institutions represented in our study had already begun implementing regular training sessions and establishing guidelines for AI system use.
The need for transparency varied across different contexts and decision types. Participants reported that for routine resource allocation decisions, such as scheduling and basic inventory management, they were generally comfortable with less detailed explanations of AI decision-making. However, for decisions affecting patient care directly or involving significant resource trade-offs, they expressed a strong need for detailed understanding of the AI’s reasoning process. Many participants described developing their own methods for verifying and validating AI recommendations in these high-stakes situations.
Participants also highlighted the practical challenges of maintaining transparency in time-sensitive situations. Several described developing quick reference guides and decision trees to help them rapidly assess AI recommendations while maintaining a basic understanding of the system’s reasoning. These practical solutions revealed how healthcare professionals actively work to balance the need for efficiency with the imperative for transparent and explicable decision-making.
Theme 3: shifting roles and responsibilities in clinical decision-making
The integration of AI-CDSS into resource allocation processes raised significant questions among participants about the changing nature of clinical decision-making and professional responsibility. Participants across all professional roles expressed complex and often conflicting views about how AI systems were reshaping their professional responsibilities and decision-making autonomy.
A critical care nurse (Participant 15) reflected, “While these systems can provide valuable insights, we can’t lose sight of the importance of human empathy and contextual understanding in healthcare decisions.” This sentiment was particularly strong among frontline healthcare providers, who frequently described situations where they felt the need to balance algorithmic recommendations against their clinical experience and understanding of patient-specific contexts.
Questions of accountability emerged as a central concern when discussing AI-CDSS involvement in resource allocation decisions. An emergency medicine physician (Participant 5) pondered, “If a decision informed by an AI system leads to a negative outcome, who bears the responsibility – the clinician, the hospital, or the system developers?” This uncertainty about accountability was especially pronounced in cases involving complex resource allocation decisions, where multiple stakeholders and competing priorities were involved.
Many participants described developing informal practices to maintain their professional autonomy while utilizing AI recommendations. These included maintaining detailed documentation of their reasoning when overriding AI suggestions, conducting regular team discussions about AI-assisted decisions, and establishing clear protocols for when human judgment should take precedence. A medical ethicist (Participant 22) suggested, “We need to develop a framework that clearly delineates the role of AI as a decision support tool, not a replacement for clinical expertise.”
The shifting nature of professional roles emerged as a particular concern among more experienced healthcare providers. Several participants with over 15 years of clinical experience described feeling challenged by the need to integrate AI recommendations into their established decision-making processes. For instance, an experienced surgeon (Participant 17) noted, “After twenty years of making these decisions based on clinical judgment, it’s not easy to suddenly start sharing that responsibility with an algorithm. We need time to adjust our professional identity to this new reality.”
The discomfort with shifting accountability was particularly evident in emergency and critical care settings. Another emergency physician (Participant 5) questioned: “If a resource allocation decision guided by AI turns out wrong, am I still held responsible? Or does the blame fall on the AI developers?” This concern was echoed across different specialties, with participants consistently expressing the need for clearer institutional guidelines about decision-making authority and professional liability.
Participants also described various strategies they had developed to maintain professional control while leveraging AI capabilities. These included creating decision checkpoints where AI recommendations would be reviewed by senior staff, establishing regular forums for discussing challenging cases, and developing departmental guidelines for AI system use. Several departments had begun implementing formal protocols to clarify the hierarchy of decision-making authority when using AI-CDSS.
Theme 4: ethical considerations in data usage and algorithm development
Participants expressed significant concerns about the ethical implications of data usage and algorithm development in AI-CDSS for resource allocation. Issues of patient privacy, consent, and data ownership emerged as primary concerns across all professional groups, with particular emphasis on the complexity of these issues in resource allocation contexts.
A primary care physician (Participant 1) voiced fundamental concerns about patient consent: “Are patients fully aware of how their data might be used in these systems, especially when it comes to influencing resource allocation decisions?” This concern was echoed by several other participants who described specific challenges in explaining to patients how their data might influence future resource allocation decisions. Some participants shared experiences of patients expressing discomfort when learning their data could affect not only their own care but also broader resource distribution decisions.
The need for diverse and representative data sets in AI-CDSS development emerged as another crucial concern. A healthcare administrator (Participant 16) noted, “If these systems are trained on data that doesn’t adequately represent our diverse patient population, we risk perpetuating or even amplifying existing health disparities.” This concern was particularly pronounced among participants working in diverse urban settings, who provided specific examples of how AI recommendations sometimes failed to account for cultural, socioeconomic, and demographic factors specific to their patient populations.
Several participants described developing their own informal monitoring systems to track potential biases in AI recommendations. For instance, a department head (Participant 11) shared: “We’ve started keeping track of cases where the AI recommendations seem misaligned with our patient population’s needs. It’s helped us identify patterns we might have missed otherwise.” These informal monitoring practices varied across institutions but generally included regular review meetings, documentation of override decisions, and tracking of outcomes in different patient subgroups.
The importance of ongoing monitoring and evaluation of AI-CDSS emerged as a key subtheme. A nurse practitioner (Participant 8) suggested, “We need robust mechanisms for continuous assessment of these systems’ impact on resource allocation and patient outcomes.” Participants described various approaches their institutions had implemented or were planning to implement, ranging from monthly audit meetings to detailed tracking systems for AI-assisted decisions.
Participants also raised concerns about data security and privacy in the context of resource allocation decisions. Several described struggling with the balance between gathering comprehensive data for improved decision-making and maintaining patient privacy. A privacy officer (Participant 19) noted: “Every additional piece of data we collect potentially improves the AI’s recommendations, but also increases our privacy obligations and risks. It’s a constant balancing act.”
Many participants emphasized the need for greater transparency in how patient data influences resource allocation algorithms. They described challenges in explaining to patients the relationship between data sharing and resource allocation decisions, with some reporting that patients became more hesitant to share data when they understood its broader implications for resource distribution.
Theme 5: balancing cost-effectiveness and patient-centered care
The final major theme that emerged from our analysis was the challenge of balancing cost-effectiveness considerations with the principles of patient-centered care when using AI-CDSS for resource allocation. Participants across different roles and specialties described complex tensions between leveraging AI for cost optimization while maintaining personalized, compassionate care delivery.
An oncologist (Participant 14) reflected, “While AI can help us identify the most cost-effective treatments, we must ensure that these recommendations don’t override individual patient preferences and values.” This sentiment was particularly strong among specialists dealing with complex or chronic conditions, where treatment decisions often involved numerous personal and contextual factors that participants felt weren’t adequately captured by AI systems.
The potential for AI-CDSS to affect the balance between financial considerations and patient care emerged as a significant concern. A medical ethicist (Participant 23) cautioned, “There’s a risk that these systems could be used to justify rationing of care under the guise of ‘optimization,’ particularly in resource-constrained settings.” This concern was especially pronounced among participants working in public healthcare facilities and other resource-limited environments, where financial pressures were already significant.
Participants described developing various strategies to maintain patient-centered care while using AI-CDSS. A family physician (Participant 11) suggested, “We need to design these systems to support, not replace, the human elements of care – empathy, communication, and shared decision-making.” Several participants shared specific examples of how they integrated AI recommendations into patient consultations while maintaining focus on individual patient needs and preferences.
The practical implementation of these principles varied across different healthcare settings. A primary care physician (Participant 3) described implementing additional checks to mitigate potential biases: “When the AI system flagged certain patients for resource allocation, I always cross-referenced with non-AI data to ensure fairness, especially in underserved populations.” This approach was echoed by other participants who had developed similar verification processes.
Organizational efforts to address these challenges were also highlighted. A hospital administrator (Participant 7) detailed their institution’s approach: “We organized workshops for our team to understand the algorithms, which helped reduce reliance on the AI as a ‘black box’ and encouraged critical engagement.” Several participants described similar initiatives at their institutions, ranging from regular team discussions about AI recommendations to formal protocols for balancing cost-effectiveness with patient needs.
Participants also emphasized the importance of maintaining flexibility in AI-assisted resource allocation decisions. Many described situations where they had to override cost-effectiveness recommendations to accommodate specific patient circumstances. A nurse manager (Participant 10) shared: “Sometimes the AI suggests the most cost-effective approach, but we know from experience that it won’t work for a particular patient’s situation. We’ve learned to trust our clinical judgment in these cases.”
The challenge of communicating cost-effectiveness decisions to patients emerged as a significant subtheme. Participants described developing various approaches to explain resource allocation decisions while maintaining trust and empathy. A palliative care specialist (Participant 25) noted: “It’s one thing to have an AI system tell you what’s cost-effective, but it’s another thing entirely to have that difficult conversation with a patient or their family. We need to maintain the human touch in these discussions.”
Table D1 in the appendix provide some additional illustrative quotes for different themes presented above. Moreover, Table E1 compares some original quotes agains the edited ones.
In conclusion, our analysis highlights healthcare professionals’ reflections on a structured set of ethical challenges concerning AI-CDSS in healthcare resource allocation, as outlined in the interview protocol. While participants shared diverse viewpoints, these were largely shaped by the predefined themes of the interview guide. This reflects a deductive approach, focusing on eliciting detailed insights into known ethical issues in the context of participants’ professional experiences.