Online selection of a physician by patients: the impression formation perspective

Online ratings and narrative comments of American Head and Neck Society surgeons

BACKGROUND

We analyzed online rating scores and comments of head and neck surgeons to understand factors that contribute to higher ratings.

METHODS

Numerical ratings and comments for American Head and Neck Society physicians were extracted from Healthgrades, Vitals, RateMDs, and Yelp, with narrative comments categorized based on content. Physician practice location, education, and residency training were also compiled.

RESULTS

Patient ratings were significantly higher with supportive staff and affable physician demeanor but showed significant drops with longer wait times and difficulties scheduling appointments or follow-ups. Physician education and postgraduate training did not significantly affect ratings.

CONCLUSION

Online ratings and comments correlated to modifiable factors in clinical practice and may be informative in understanding patient needs.

Research on the doctors' win in crowdsourcing competitions: perspectives on service content and competitive environment

Medical crowdsourcing competitions can help patients get more efficient and comprehensive treatment advice than "one-to-one" service, and doctors should be encouraged to actively participate. In the crowdsourcing competitions, winning the crowdsourcing competition is the driving force for doctors to continue to participate in the service. Therefore, how to improve the winning probability needs to be revealed. From the service content and competitive environment perspectives, this study introduces doctor competence indicators to investigate the key influence factors of doctors' wins on the online platform. The results show that the emotional interaction in doctors' service content positively influences doctors' wins. However, the influence of information interaction presents heterogeneity. Conclusive information helps doctors win, while suggestive information negatively affects them. For the competitive environment, the competitive environment negatively moderates the relationship between doctors' service content and doctors' wins. The results of this study provide important contributions to the research on crowdsourcing competitions and online healthcare services and guide the participants of the competition, including patients, doctors, and platforms.

Patient's behavior of selection physician in online health communities: Based on an Elaboration likelihood model

Background

With the rapid development of "Internet + medicine" and the impact of the COVID-19 epidemic, online health communities have become an important way for patients to seek medical treatment. However, the mistrust between physicians and patients in online health communities has long existed and continues to impact the decision-making behavior of patients. The purpose of this article is to explore the influencing factors of patient decision-making in online health communities by identifying the relationship between physicians' online information and patients' selection behavior.

Methods

In this study, we selected China's Good Doctor (www.haodf.com) as the source of data, scrapped 10,446 physician data from December 2020 to June 2021 to construct a logit model of online patients' selection behavior, and used regression analysis to test the hypotheses.

Results

The number of types of services, number of scientific articles, and avatar in physicians' personal information all has a positive effect on patients' selection behavior, while the title and personal introduction hurt patients' selection behavior. Online word-of-mouth positively affected patients' selection behavior and disease risk had a moderating effect.

Conclusion

Focusing on physician-presented information, this article organically combines the Elaboration likelihood model with trust source theory and online word-of-mouth from the perspective of the trusted party-physician, providing new ideas for the study of factors influencing patients' selection behavior in online health communities. The findings provide useful insights for patients, physicians, and community managers about the relationship between physician information and patients' selection behavior.