AI in Patient Communication: Implications for
Safety, Quality, and Epistemic Justice
[name]
Student Number [XXX]
Teacher: [name]
27 – 11 – 2025
2200 words
Introduction
, By 2030 the global demand for health workers will increase to 80 million, resulting in an estimated
shortage of around 15 million professionals (Liu et al., 2017). Together with rising patient demands,
this creates serious risks: clinicians face higher workloads and higher chances of burnout, which
reduces their capacity to provide safe, high-quality care (Pollack et al., 2023; Naseem et al., 2024).
Besides that, chronic conditions have increased significantly, making the shift toward more
preventive, lifestyle-oriented care urgent (Rodrigues Da Silva et al., 2024). For this reason, healthcare
organizations and policymakers are increasingly exploring AI-driven tools to automate administrative
tasks, manage patient communication, and free up time for clinicians, thereby reducing workload
(Varnosfaderani & Forouzanfar, 2024; Saeed & Nashwan, 2025).
An example of how AI has been integrated in preventive care is Lampie; an AI lifestyle assistant
implemented at Elkerliek Hospital in Helmond, which provides evidence-based lifestyle advice to
patients (Skipr, 2025). While tools like Lampie could be a potential solution to workforce shortages
and rising patient demands, it also raises questions about care quality, safety, and equity, as studies
show that AI systems can unintentionally reinforce existing biases or overlook patients whose
experiences are not well represented in structured datasets (Garba-Sani et al., 2024). Therefore, the
integration of AI must be done with caution, considering the social, cultural, and ethical dimensions
that influence the quality and safety of patient care.
This brings us to the central question guiding this essay:
How does the integration of AI-driven tools into patient communication influence hospitals’ ability to
deliver safe, high-quality, and epistemically just care, and how do organizational practices and
structures shape this integration?
The societal relevance of this question is clear: as hospitals increasingly rely on AI to improve
efficiency and support patient communication, understanding its broader implications is important to
avoid unintended harm and ensure equitable and safe care.
To answer this question, this essay is structured as follows. First, a conceptual framework is
developed, bringing together four complementary lenses: Smits’ monster theory, Carel & Kidd’s
notion of epistemic injustice, Bromley & Powell’s idea of decoupling, and Clegg et al.’s work on
organizational learning and becoming. Instead of treating these theories separately, as has been done
in previous research, this framework will combine them to show how cultural perceptions, existing
inequalities, and organizational structures together influence how AI affects quality and safety of care.
2
Safety, Quality, and Epistemic Justice
[name]
Student Number [XXX]
Teacher: [name]
27 – 11 – 2025
2200 words
Introduction
, By 2030 the global demand for health workers will increase to 80 million, resulting in an estimated
shortage of around 15 million professionals (Liu et al., 2017). Together with rising patient demands,
this creates serious risks: clinicians face higher workloads and higher chances of burnout, which
reduces their capacity to provide safe, high-quality care (Pollack et al., 2023; Naseem et al., 2024).
Besides that, chronic conditions have increased significantly, making the shift toward more
preventive, lifestyle-oriented care urgent (Rodrigues Da Silva et al., 2024). For this reason, healthcare
organizations and policymakers are increasingly exploring AI-driven tools to automate administrative
tasks, manage patient communication, and free up time for clinicians, thereby reducing workload
(Varnosfaderani & Forouzanfar, 2024; Saeed & Nashwan, 2025).
An example of how AI has been integrated in preventive care is Lampie; an AI lifestyle assistant
implemented at Elkerliek Hospital in Helmond, which provides evidence-based lifestyle advice to
patients (Skipr, 2025). While tools like Lampie could be a potential solution to workforce shortages
and rising patient demands, it also raises questions about care quality, safety, and equity, as studies
show that AI systems can unintentionally reinforce existing biases or overlook patients whose
experiences are not well represented in structured datasets (Garba-Sani et al., 2024). Therefore, the
integration of AI must be done with caution, considering the social, cultural, and ethical dimensions
that influence the quality and safety of patient care.
This brings us to the central question guiding this essay:
How does the integration of AI-driven tools into patient communication influence hospitals’ ability to
deliver safe, high-quality, and epistemically just care, and how do organizational practices and
structures shape this integration?
The societal relevance of this question is clear: as hospitals increasingly rely on AI to improve
efficiency and support patient communication, understanding its broader implications is important to
avoid unintended harm and ensure equitable and safe care.
To answer this question, this essay is structured as follows. First, a conceptual framework is
developed, bringing together four complementary lenses: Smits’ monster theory, Carel & Kidd’s
notion of epistemic injustice, Bromley & Powell’s idea of decoupling, and Clegg et al.’s work on
organizational learning and becoming. Instead of treating these theories separately, as has been done
in previous research, this framework will combine them to show how cultural perceptions, existing
inequalities, and organizational structures together influence how AI affects quality and safety of care.
2
