# Association Between Physician Communication Features and Patient Outcomes in Telemedicine: Retrospective Cross-Sectional Observational Study

## TL;DR

This study shows how doctors' communication styles in telemedicine affect patient satisfaction and return visits, using real-world data from thousands of virtual consultations.

## Contribution

The study introduces a novel hybrid approach using large-scale real-world data to analyze physician communication features in telemedicine, going beyond prior survey-based methods.

## Key findings

- Audio-only visits were linked to lower patient loyalty compared to text-only visits.
- Visits starting with a short audio response and ending with text had higher patient loyalty.
- More text and audio replies were marginally associated with improved patient return visits.

## Abstract

Asynchronous telemedicine is a crucial component of multichannel health care, where effective communication drives satisfaction. However, the effectiveness of communication features remains poorly understood. Prior research relied on subjective surveys or small-scale simulations, failing to link features to objective outcomes. Understanding these features is critical for optimizing physician engagement and establishing quality indicators to enhance the patient experience.

This study aimed to bridge this gap by leveraging a large-scale real-world dataset to quantify the association between physicians’ communication features—including response modalities, length, and sequence—and patient repurchase behavior, as well as review scores, within a high-autonomy health care setting.

This retrospective cross-sectional study analyzed 304,337 paid, patient-initiated virtual visits from a Chinese academic medical center (2021‐2023), which included 823,135 physician responses. The sample was selected after applying a series of exclusion criteria, such as free consultations, team-based visits, and outlier data. The key exposures were the modality of physician responses, response length, and response sequence. Outcomes included patient loyalty and satisfaction. Loyalty was operationalized as follow-up visits within 6 months, with a 30-day exclusion period applied to same-physician (fv1) and same-department (fv2) revisits to filter out clinical necessity, but not to hospital-wide revisits (fv3). Satisfaction was measured by the review scores. We used probit and ordinary least squares regressions to examine the relationships between communication features and patient outcomes.

Regarding loyalty, audio-only visits were associated with the lowest fv1, with an average marginal effect (AME) of −0.030 (95% CI −0.043 to −0.016, P<.001), translating to a 30.9% (0.030/0.097) reduction compared to text-only visits. Regarding satisfaction, audio messages were associated with a significantly increased likelihood of patients providing reviews, with an AME of 0.041 (95% CI 0.006‐0.076, P=.02), but they did not affect review scores after adjusting for inverse Mills ratios. Increased numbers of text and audio replies were (marginally) associated with improved fv1, with AMEs of 0.009 (95% CI 0.006‐0.011, P<.001) and 0.007 (95% CI −0.000 to 0.016, P=.06), respectively. Visits beginning with a sub-5-second audio response and ending with text had significantly higher fv1 than text-only visits, with an AME of 0.069 (95% CI 0.018‐0.120, P=.008). The same patterns hold for fv2 and fv3. Based on the Bonferroni test, coefficients with a P value smaller than α=.050/3=.017 or α=.50/2=.025 were regarded as significant when evaluating the association with patient loyalty or satisfaction, respectively.

Physician communication practices were significantly associated with patient loyalty and satisfaction. This study is innovative in leveraging large-scale real-world data to systematically examine physician communication. It differs from existing studies by transcending prior survey-based research limitations. It introduces an effective hybrid approach, balancing human connection with text clarity in the field. Its implication in the real world is providing data-driven evidence to guide clinicians and policymakers in designing high-quality telemedicine services.