Where Can Artificial Intelligence Assist Cancer Care?: Examining Patient-Centered Communication Dimension Effects.

OBJECTIVE

To explore how aspects of patient-centered communication (PCC) may directly or indirectly predict patients' preferences for artificial intelligences (AIs) versus human medical professionals, based on the stimulus-organism-response model.

STUDY SETTING AND

DESIGN

As AI gains popularity and researchers explore its application in the medical context, it is important to understand how current patient-provider dynamics involving high technology (e.g., telehealth communication) may shape patients' perceptions of future use of AI, especially in the context of cancer care where patient satisfaction and sense of care continuity are important. Participants were recruited from an online panel in China (June 2024). Structural equation modeling analyzed the relationships among variables, including six PCC dimensions (i.e., exchanging information, fostering healing relationships, making decisions, managing uncertainty, responding to emotions, and enabling patient self-management), communication outcomes (i.e., patient satisfaction, sense of care continuity), and patients' preference of AIs vs. human medical professionals.

DATA SOURCES AND ANALYTIC SAMPLE: Primary data were collected from an online panel of 495 Chinese cancer patients in China, representative of the gender and age distribution of the overall Chinese population due to quota sampling.

PRINCIPAL FINDINGS: Direct predictors of preference for replacing human medical professionals with AIs included lower patient satisfaction (β = -11, p < 0.05), lower ease of use (β = -0.1, p < 0.05), better care continuity (β = 0.15, p < 0.01), providers' attending to emotions (β = 0.17, p < 0.05), and less enablement in self-management (β = -0.17, p < 0.01). Patient satisfaction, ease of use, and care continuity mediated the relationships between different PCC dimensions and patients' preferences for AI use.

CONCLUSIONS

PCC and communication outcomes are associated with cancer patients' preferences in future AI use. Our study sheds light on how clinicians may improve their communication to educate patients on navigating the cancer care continuum using AI technology.