Digital self-service in hospitals and patient treatment barriers: The role of organizational trust via dual mechanisms

We read with great interest the article by Luxin Zhang et al. ¹ examining trust transfer in digital healthcare and the role of self-service systems in reducing patient treatment barriers. The authors demonstrate that patients’ interactions with hospital self-service technologies shape their organizational trust, which diminishes both psychological doubts and practical obstacles to treatment adherence. A strength of this study lies in its innovative application of trust transfer theory within digital healthcare. Zhang et al. integrate trust transfer theory with the Organizational Trust Model and provide empirical evidence for both the direct and mediated pathways through which technological trust translates into organizational trust. ² Specifically, the authors show that patients’ trust in self-service technologies transfers to trust in healthcare providers via two mechanisms—one driven by the immediate convenience of the system and the other by the perception of system reliability shaped by safety and information quality. This dual-path validation enhances the explanatory power of trust transfer theory and deepens our understanding of how technological interactions serve as critical touchpoints in institutional trust formation. ³

However, the use of the “random distribution” mechanism by WenJuanXing (wjx.cn) raises methodological concerns. WenJuanXing randomly samples from its own platform users rather than from the target population of hospital patients, constituting a form of convenience sampling that may introduce selection bias. ⁴ Platform users differ systematically from actual patient populations in terms of health status, digital literacy, care-seeking behavior, and prior exposure to medical self-service systems. Consequently, the WenJuanXing subsample may not adequately represent the intended study population, limiting external validity and generalizability. ⁵ Future research should consider more targeted sampling strategies—such as stratified recruitment within clinical settings or disease-specific cohorts—to ensure cross-sectional data more accurately capture the characteristics of the patient population. Future studies could also specify the geographical and institutional context (e.g. focusing on a particular county or prefecture in China, the specific hospital level—tertiary vs. secondary—and the sample of 310 patients). Two avenues are recommended. First, a regional comparison study could explore how differences in digitalization across regions affect trust transfer mechanisms and treatment adherence outcomes, informing macro-level policy recommendations for digital health equity. ⁶ Second, a hospital-scale comparison study could reveal how hospitals of different sizes vary in their digital transformation processes, proposing tailored improvement strategies for various institutions. ⁷ By adopting these designs, future studies can enhance the contextual generalizability of the model and provide actionable guidance for policymakers and healthcare managers. We appreciate the opportunity to comment on Zhang et al. ¹ Addressing the methodological and contextual limitations we noted—particularly regarding sampling strategy and institutional/regional specificity—would further reinforce the empirical robustness and practical utility of the model. We look forward to future empirical studies that build upon Zhang et al. using more rigorous sampling frameworks across diverse institutional and regional settings.