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Communication Patterns and Patient Satisfaction in Online Cardiovascular Consultations: A Mixed-Methods Analysis
ABSTRACT
Background:
Background:
Digital health technologies have made online physician-patient communication pivotal for cardiovascular disease (CVDs) management. CVDs, major global causes of morbidity and mortality, require long-term follow-up and entail patients’ dual biomedical and psychosocial needs. Patient-centered care (PCC) is foundational but challenged by nonverbal cue loss in text-based online settings. Existing research lacks disease-specific insights into communication patterns in CVDs telemedicine, and how such patterns influence patient satisfaction.
Objective:
Objective:
This study aims to identify typical communication patterns in online CVDs consultations, analyze their compositional features, and examine their association with patient satisfaction, thereby providing practical implications for optimizing remote cardiovascular care.
Methods:
Design: Mixed methods study design answered the study questions.
Results:
Results:
A total of 2559 doctor utterances and 2738 patient utterances were coded using the Roter Interaction Analysis System (RIAS). Four distinct communication patterns were identified: Relation-Supported, Deep Empathy, High-Task Integrated, and Efficiency-Oriented. Representative dialogues for each pattern were excerpted and presented to illustrate their differing combinations of emotional engagement and task focus. Compared with the Efficiency-Oriented pattern, the Deep Empathy (OR = 13.731, 95% CI: 2.493-75.624, P = 0.003), Relation-Supported (OR = 7.929, 95% CI: 3.352-18.752, P<0.001), and High-Task Integrated (OR = 4.291, 95% CI: 2.105-8.744, P<0.001) patterns all showed significant positive effects on patient satisfaction, with varying magnitudes of effect.
Conclusions:
Conclusions:
This study makes notable innovations by identifying four distinct communication patterns in online cardiovascular consultations using RIAS-based mixed methods, challenging the traditional dichotomous classification of physician-patient interactions as merely task- or emotion-oriented. Unlike prior research limited to offline or general online settings, we confirm a clear gradient effect of socioemotional engagement on patient satisfaction, with the Relation-Supported pattern as a robust and feasible approach, while the Deep Empathy pattern is particularly effective for patients with specific emotional needs. Gender disparities further underscore the value of tailored communication. These findings advance patient-centered care in digital chronic disease management and offer evidence-based implications for clinician training, platform optimization, and precision communication strategies.
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Copyright
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